{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named oceanmodes",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m\u001b[0m",
      "\u001b[1;31mImportError\u001b[0mTraceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-ab9715d81fdb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mfftpack\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfft\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptimize\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcurve_fit\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0moceanmodes\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mbaroclinic\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[0mreload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbaroclinic\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: No module named oceanmodes"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import xarray as xray\n",
    "import gsw\n",
    "import os\n",
    "from scipy import io\n",
    "from scipy import fftpack as fft\n",
    "from scipy.optimize import curve_fit\n",
    "from oceanmodes import baroclinic\n",
    "reload(baroclinic)\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "basedir = '/data/scratch/takaya/OCCA/annual'\n",
    "fname_eta_surf = os.path.join(basedir, 'DDetan.0406annclim.nc')\n",
    "fname_potP_bot = os.path.join(basedir, 'DDphibot.0406annclim.nc')\n",
    "fname_rhoanom = os.path.join(basedir, 'DDrhoan.0406annclim.nc')\n",
    "fname_u = os.path.join(basedir, 'DDuvel.0406annclim.nc')\n",
    "fname_v = os.path.join(basedir, 'DDvvel.0406annclim.nc')\n",
    "fname_s = os.path.join(basedir, 'DDsalt.0406annclim.nc')\n",
    "fname_t = os.path.join(basedir, 'DDtheta.0406annclim.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nc_eta_surf = xray.open_dataset(fname_eta_surf)\n",
    "nc_potP_bot = xray.open_dataset(fname_potP_bot)\n",
    "nc_rhoanom = xray.open_dataset(fname_rhoanom)\n",
    "nc_u = xray.open_dataset(fname_u)\n",
    "nc_v = xray.open_dataset(fname_v)\n",
    "nc_s = xray.open_dataset(fname_s)\n",
    "nc_t = xray.open_dataset(fname_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "eta_s = nc_eta_surf.etan[0]\n",
    "potP_b = nc_potP_bot.phibot[0]\n",
    "rho_anom = nc_rhoanom.rhoanoma[0]\n",
    "u = nc_u.u[0]\n",
    "v = nc_v.v[0]\n",
    "sal = nc_s.salt[0]\n",
    "theta = nc_t.theta[0]\n",
    "lat_t = nc_s.Latitude_t\n",
    "lon_t = nc_s.Longitude_t\n",
    "lat_v = nc_v.Latitude_v\n",
    "lon_u = nc_u.Longitude_u\n",
    "z_t = nc_s.Depth_c\n",
    "z_u = nc_u.Depth_u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'u' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m\u001b[0m",
      "\u001b[1;31mNameError\u001b[0mTraceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-37547f1e18f5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0mu\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mz_t\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mlat_t\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlat_v\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mlon_t\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlon_u\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'u' is not defined"
     ]
    }
   ],
   "source": [
    "print u, z_t\n",
    "print lat_t, lat_v\n",
    "print lon_t, lon_u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(160, 360) (50, 160, 360) (50,) (160,) (360,)\n",
      "(50, 160, 360)\n"
     ]
    }
   ],
   "source": [
    "print eta_s.shape, sal.shape, z_t.shape, lat_t.shape, lon_t.shape\n",
    "print v.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "p_t = gsw.p_from_z(-z_t.values[:, np.newaxis], lat_t.values[np.newaxis, :])\n",
    "absS = gsw.SA_from_SP(sal.values, p_t[:, :, np.newaxis], \n",
    "                      lon_t.values[np.newaxis, np.newaxis, :], lat_t.values[np.newaxis, :, np.newaxis])\n",
    "consT = gsw.CT_from_pt(absS, theta.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 160, 360) (50, 160, 360)\n"
     ]
    }
   ],
   "source": [
    "print absS.shape, consT.shape\n",
    "# print p_t, absS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "potrho = gsw.rho(absS, consT, 0.)\n",
    "N2, p_N2 = gsw.Nsquared(absS, consT, \n",
    "                        p_t[:, :, np.newaxis], lat_t.values[np.newaxis, :, np.newaxis])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "z_N2 = gsw.z_from_p(p_N2, lat_t.values[np.newaxis, :, np.newaxis])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# print z_N2[:, 0, 0], z_u[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f0 = gsw.earth.f(lat_t.values)\n",
    "beta = 2.*gsw.earth.OMEGA/gsw.earth.earth_radius * np.cos(np.pi/180.*lat_t.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "absS_meta = xray.DataArray(absS, coords=theta.coords, dims=theta.dims)\n",
    "consT_meta = xray.DataArray(consT, coords=theta.coords, dims=theta.dims)\n",
    "N2_meta = xray.DataArray(N2, coords=theta[1:].coords, dims=theta[1:].dims)\n",
    "potrho_meta = xray.DataArray(potrho, coords=theta.coords, dims=theta.dims)\n",
    "zN2_meta = xray.DataArray(z_N2, coords=theta[1:].coords, dims=theta[1:].dims)\n",
    "f0_meta = xray.DataArray(f0, coords=lat_t.coords, dims=lat_t.dims)\n",
    "beta_meta = xray.DataArray(beta, coords=lat_t.coords, dims=lat_t.dims)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# f0_meta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rigid lid & flat ocean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'Longitude_u' (Longitude_u: 360)>\n",
      "array([   0.,    1.,    2.,    3.,    4.,    5.,    6.,    7.,    8.,\n",
      "          9.,   10.,   11.,   12.,   13.,   14.,   15.,   16.,   17.,\n",
      "         18.,   19.,   20.,   21.,   22.,   23.,   24.,   25.,   26.,\n",
      "         27.,   28.,   29.,   30.,   31.,   32.,   33.,   34.,   35.,\n",
      "         36.,   37.,   38.,   39.,   40.,   41.,   42.,   43.,   44.,\n",
      "         45.,   46.,   47.,   48.,   49.,   50.,   51.,   52.,   53.,\n",
      "         54.,   55.,   56.,   57.,   58.,   59.,   60.,   61.,   62.,\n",
      "         63.,   64.,   65.,   66.,   67.,   68.,   69.,   70.,   71.,\n",
      "         72.,   73.,   74.,   75.,   76.,   77.,   78.,   79.,   80.,\n",
      "         81.,   82.,   83.,   84.,   85.,   86.,   87.,   88.,   89.,\n",
      "         90.,   91.,   92.,   93.,   94.,   95.,   96.,   97.,   98.,\n",
      "         99.,  100.,  101.,  102.,  103.,  104.,  105.,  106.,  107.,\n",
      "        108.,  109.,  110.,  111.,  112.,  113.,  114.,  115.,  116.,\n",
      "        117.,  118.,  119.,  120.,  121.,  122.,  123.,  124.,  125.,\n",
      "        126.,  127.,  128.,  129.,  130.,  131.,  132.,  133.,  134.,\n",
      "        135.,  136.,  137.,  138.,  139.,  140.,  141.,  142.,  143.,\n",
      "        144.,  145.,  146.,  147.,  148.,  149.,  150.,  151.,  152.,\n",
      "        153.,  154.,  155.,  156.,  157.,  158.,  159.,  160.,  161.,\n",
      "        162.,  163.,  164.,  165.,  166.,  167.,  168.,  169.,  170.,\n",
      "        171.,  172.,  173.,  174.,  175.,  176.,  177.,  178.,  179.,\n",
      "        180.,  181.,  182.,  183.,  184.,  185.,  186.,  187.,  188.,\n",
      "        189.,  190.,  191.,  192.,  193.,  194.,  195.,  196.,  197.,\n",
      "        198.,  199.,  200.,  201.,  202.,  203.,  204.,  205.,  206.,\n",
      "        207.,  208.,  209.,  210.,  211.,  212.,  213.,  214.,  215.,\n",
      "        216.,  217.,  218.,  219.,  220.,  221.,  222.,  223.,  224.,\n",
      "        225.,  226.,  227.,  228.,  229.,  230.,  231.,  232.,  233.,\n",
      "        234.,  235.,  236.,  237.,  238.,  239.,  240.,  241.,  242.,\n",
      "        243.,  244.,  245.,  246.,  247.,  248.,  249.,  250.,  251.,\n",
      "        252.,  253.,  254.,  255.,  256.,  257.,  258.,  259.,  260.,\n",
      "        261.,  262.,  263.,  264.,  265.,  266.,  267.,  268.,  269.,\n",
      "        270.,  271.,  272.,  273.,  274.,  275.,  276.,  277.,  278.,\n",
      "        279.,  280.,  281.,  282.,  283.,  284.,  285.,  286.,  287.,\n",
      "        288.,  289.,  290.,  291.,  292.,  293.,  294.,  295.,  296.,\n",
      "        297.,  298.,  299.,  300.,  301.,  302.,  303.,  304.,  305.,\n",
      "        306.,  307.,  308.,  309.,  310.,  311.,  312.,  313.,  314.,\n",
      "        315.,  316.,  317.,  318.,  319.,  320.,  321.,  322.,  323.,\n",
      "        324.,  325.,  326.,  327.,  328.,  329.,  330.,  331.,  332.,\n",
      "        333.,  334.,  335.,  336.,  337.,  338.,  339.,  340.,  341.,\n",
      "        342.,  343.,  344.,  345.,  346.,  347.,  348.,  349.,  350.,\n",
      "        351.,  352.,  353.,  354.,  355.,  356.,  357.,  358.,  359.], dtype=float32)\n",
      "Coordinates:\n",
      "  * Longitude_u  (Longitude_u) float32 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 ...\n",
      "Attributes:\n",
      "    units: degree          \n",
      "    long_name: Longitude on U grid                                                     \n",
      "    modulo: 360 <xarray.DataArray 'Longitude_t' (Longitude_t: 360)>\n",
      "array([   0.5,    1.5,    2.5,    3.5,    4.5,    5.5,    6.5,    7.5,\n",
      "          8.5,    9.5,   10.5,   11.5,   12.5,   13.5,   14.5,   15.5,\n",
      "         16.5,   17.5,   18.5,   19.5,   20.5,   21.5,   22.5,   23.5,\n",
      "         24.5,   25.5,   26.5,   27.5,   28.5,   29.5,   30.5,   31.5,\n",
      "         32.5,   33.5,   34.5,   35.5,   36.5,   37.5,   38.5,   39.5,\n",
      "         40.5,   41.5,   42.5,   43.5,   44.5,   45.5,   46.5,   47.5,\n",
      "         48.5,   49.5,   50.5,   51.5,   52.5,   53.5,   54.5,   55.5,\n",
      "         56.5,   57.5,   58.5,   59.5,   60.5,   61.5,   62.5,   63.5,\n",
      "         64.5,   65.5,   66.5,   67.5,   68.5,   69.5,   70.5,   71.5,\n",
      "         72.5,   73.5,   74.5,   75.5,   76.5,   77.5,   78.5,   79.5,\n",
      "         80.5,   81.5,   82.5,   83.5,   84.5,   85.5,   86.5,   87.5,\n",
      "         88.5,   89.5,   90.5,   91.5,   92.5,   93.5,   94.5,   95.5,\n",
      "         96.5,   97.5,   98.5,   99.5,  100.5,  101.5,  102.5,  103.5,\n",
      "        104.5,  105.5,  106.5,  107.5,  108.5,  109.5,  110.5,  111.5,\n",
      "        112.5,  113.5,  114.5,  115.5,  116.5,  117.5,  118.5,  119.5,\n",
      "        120.5,  121.5,  122.5,  123.5,  124.5,  125.5,  126.5,  127.5,\n",
      "        128.5,  129.5,  130.5,  131.5,  132.5,  133.5,  134.5,  135.5,\n",
      "        136.5,  137.5,  138.5,  139.5,  140.5,  141.5,  142.5,  143.5,\n",
      "        144.5,  145.5,  146.5,  147.5,  148.5,  149.5,  150.5,  151.5,\n",
      "        152.5,  153.5,  154.5,  155.5,  156.5,  157.5,  158.5,  159.5,\n",
      "        160.5,  161.5,  162.5,  163.5,  164.5,  165.5,  166.5,  167.5,\n",
      "        168.5,  169.5,  170.5,  171.5,  172.5,  173.5,  174.5,  175.5,\n",
      "        176.5,  177.5,  178.5,  179.5,  180.5,  181.5,  182.5,  183.5,\n",
      "        184.5,  185.5,  186.5,  187.5,  188.5,  189.5,  190.5,  191.5,\n",
      "        192.5,  193.5,  194.5,  195.5,  196.5,  197.5,  198.5,  199.5,\n",
      "        200.5,  201.5,  202.5,  203.5,  204.5,  205.5,  206.5,  207.5,\n",
      "        208.5,  209.5,  210.5,  211.5,  212.5,  213.5,  214.5,  215.5,\n",
      "        216.5,  217.5,  218.5,  219.5,  220.5,  221.5,  222.5,  223.5,\n",
      "        224.5,  225.5,  226.5,  227.5,  228.5,  229.5,  230.5,  231.5,\n",
      "        232.5,  233.5,  234.5,  235.5,  236.5,  237.5,  238.5,  239.5,\n",
      "        240.5,  241.5,  242.5,  243.5,  244.5,  245.5,  246.5,  247.5,\n",
      "        248.5,  249.5,  250.5,  251.5,  252.5,  253.5,  254.5,  255.5,\n",
      "        256.5,  257.5,  258.5,  259.5,  260.5,  261.5,  262.5,  263.5,\n",
      "        264.5,  265.5,  266.5,  267.5,  268.5,  269.5,  270.5,  271.5,\n",
      "        272.5,  273.5,  274.5,  275.5,  276.5,  277.5,  278.5,  279.5,\n",
      "        280.5,  281.5,  282.5,  283.5,  284.5,  285.5,  286.5,  287.5,\n",
      "        288.5,  289.5,  290.5,  291.5,  292.5,  293.5,  294.5,  295.5,\n",
      "        296.5,  297.5,  298.5,  299.5,  300.5,  301.5,  302.5,  303.5,\n",
      "        304.5,  305.5,  306.5,  307.5,  308.5,  309.5,  310.5,  311.5,\n",
      "        312.5,  313.5,  314.5,  315.5,  316.5,  317.5,  318.5,  319.5,\n",
      "        320.5,  321.5,  322.5,  323.5,  324.5,  325.5,  326.5,  327.5,\n",
      "        328.5,  329.5,  330.5,  331.5,  332.5,  333.5,  334.5,  335.5,\n",
      "        336.5,  337.5,  338.5,  339.5,  340.5,  341.5,  342.5,  343.5,\n",
      "        344.5,  345.5,  346.5,  347.5,  348.5,  349.5,  350.5,  351.5,\n",
      "        352.5,  353.5,  354.5,  355.5,  356.5,  357.5,  358.5,  359.5], dtype=float32)\n",
      "Coordinates:\n",
      "  * Longitude_t  (Longitude_t) float32 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 ...\n",
      "Attributes:\n",
      "    units: degree_east     \n",
      "    long_name: Longitude on T grid                                                     \n",
      "    standard_name: longitude\n",
      "    modulo:  \n",
      "    point_spacing: even\n",
      "<xarray.DataArray 'Latitude_v' (Latitude_v: 160)>\n",
      "array([-80., -79., -78., -77., -76., -75., -74., -73., -72., -71., -70.,\n",
      "       -69., -68., -67., -66., -65., -64., -63., -62., -61., -60., -59.,\n",
      "       -58., -57., -56., -55., -54., -53., -52., -51., -50., -49., -48.,\n",
      "       -47., -46., -45., -44., -43., -42., -41., -40., -39., -38., -37.,\n",
      "       -36., -35., -34., -33., -32., -31., -30., -29., -28., -27., -26.,\n",
      "       -25., -24., -23., -22., -21., -20., -19., -18., -17., -16., -15.,\n",
      "       -14., -13., -12., -11., -10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,\n",
      "        -3.,  -2.,  -1.,   0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,\n",
      "         8.,   9.,  10.,  11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,\n",
      "        19.,  20.,  21.,  22.,  23.,  24.,  25.,  26.,  27.,  28.,  29.,\n",
      "        30.,  31.,  32.,  33.,  34.,  35.,  36.,  37.,  38.,  39.,  40.,\n",
      "        41.,  42.,  43.,  44.,  45.,  46.,  47.,  48.,  49.,  50.,  51.,\n",
      "        52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.,  60.,  61.,  62.,\n",
      "        63.,  64.,  65.,  66.,  67.,  68.,  69.,  70.,  71.,  72.,  73.,\n",
      "        74.,  75.,  76.,  77.,  78.,  79.], dtype=float32)\n",
      "Coordinates:\n",
      "  * Latitude_v  (Latitude_v) float32 -80.0 -79.0 -78.0 -77.0 -76.0 -75.0 ...\n",
      "Attributes:\n",
      "    units: degree          \n",
      "    long_name: Latitude on V grid                                                       <xarray.DataArray 'Latitude_t' (Latitude_t: 160)>\n",
      "array([-79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5,\n",
      "       -70.5, -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5,\n",
      "       -61.5, -60.5, -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5,\n",
      "       -52.5, -51.5, -50.5, -49.5, -48.5, -47.5, -46.5, -45.5, -44.5,\n",
      "       -43.5, -42.5, -41.5, -40.5, -39.5, -38.5, -37.5, -36.5, -35.5,\n",
      "       -34.5, -33.5, -32.5, -31.5, -30.5, -29.5, -28.5, -27.5, -26.5,\n",
      "       -25.5, -24.5, -23.5, -22.5, -21.5, -20.5, -19.5, -18.5, -17.5,\n",
      "       -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,  -9.5,  -8.5,\n",
      "        -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,   0.5,\n",
      "         1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
      "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,\n",
      "        19.5,  20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,\n",
      "        28.5,  29.5,  30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,\n",
      "        37.5,  38.5,  39.5,  40.5,  41.5,  42.5,  43.5,  44.5,  45.5,\n",
      "        46.5,  47.5,  48.5,  49.5,  50.5,  51.5,  52.5,  53.5,  54.5,\n",
      "        55.5,  56.5,  57.5,  58.5,  59.5,  60.5,  61.5,  62.5,  63.5,\n",
      "        64.5,  65.5,  66.5,  67.5,  68.5,  69.5,  70.5,  71.5,  72.5,\n",
      "        73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5], dtype=float32)\n",
      "Coordinates:\n",
      "  * Latitude_t  (Latitude_t) float32 -79.5 -78.5 -77.5 -76.5 -75.5 -74.5 ...\n",
      "Attributes:\n",
      "    units: degree_north    \n",
      "    long_name: Latitude on T grid                                                      \n",
      "    standard_name: latitude\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f4b50eee350>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P/43Afwb+fo5j9w3nhwBp0KBBg33ETozoqpqKyOuBj2NZt/eo6o0i8jq7W9+lqjeJyB8C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\n4q6NU/R6vp+ZLwEAMz9ORPffRBvfR0TfAeB3AfxgGwW2KZxUgJx0dv4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Rq7+ZcLzlNFnj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KsFiETda+ExGKXZjKHoqsx2lVwrFK2m1wYZZZofc+SCLkPbok1/3ySsSq3zdMaZECC1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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4b8a6e2f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print lon_u, lon_t\n",
    "print lat_v, lat_t\n",
    "xray.plot.imshow((.5*(u.roll(Longitude_u=1) + u))[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "u_coinT = xray.DataArray((.5*(u.roll(Longitude_u=-1) + u)),\n",
    "                      coords=theta.coords, dims=theta.dims)\n",
    "v_coinT = xray.DataArray((.5*(v.roll(Latitude_v=-1) + v)),\n",
    "                      coords=theta.coords, dims=theta.dims)\n",
    "# print u, u_coinT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gulf Stream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "GS = np.array([299.5, 39.5])\n",
    "u_GS = u_coinT.sel(Longitude_t=GS[0], Latitude_t=GS[1])\n",
    "v_GS = v_coinT.sel(Longitude_t=GS[0], Latitude_t=GS[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "absS_GS = absS_meta.sel(Longitude_t=GS[0], Latitude_t=GS[1])\n",
    "consT_GS = consT_meta.sel(Longitude_t=GS[0], Latitude_t=GS[1])\n",
    "rho_GS = rho_anom.sel(Longitude_t=GS[0], Latitude_t=GS[1])\n",
    "potrho_GS = potrho_meta.sel(Longitude_t=GS[0], Latitude_t=GS[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "N2_GS = N2_meta.sel(Longitude_t=GS[0], Latitude_t=GS[1])\n",
    "zN2_GS = zN2_meta.sel(Longitude_t=GS[0], Latitude_t=GS[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray (Depth_c: 50)>\n",
      "array([ 36.37705617,  36.38829268,  36.43808331,  36.49948204,\n",
      "        36.53953381,  36.55776704,  36.56630143,  36.57135183,\n",
      "        36.57744907,  36.57823244,  36.57765099,  36.57286845,\n",
      "        36.56452534,  36.55226472,  36.53229802,  36.50537004,\n",
      "        36.46777153,  36.41438766,  36.34106086,  36.24866002,\n",
      "        36.1329151 ,  35.98797394,  35.82308989,  35.66429781,\n",
      "        35.51827826,  35.39446247,  35.29708428,  35.22529779,\n",
      "        35.17724572,  35.15076161,  35.14115192,  35.14007741,\n",
      "        35.13925532,  35.13451384,  35.12538326,  35.11673309,\n",
      "        35.11542681,  35.12178446,  35.12816684,  35.12755937,\n",
      "        35.12004502,  35.11000069,  35.10029747,  35.09019658,\n",
      "        35.0800635 ,  35.07483442,  35.07510403,  35.07778868,\n",
      "                nan,          nan])\n",
      "Coordinates:\n",
      "    Longitude_t  float32 299.5\n",
      "  * Depth_c      (Depth_c) float32 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Latitude_t   float32 39.5\n",
      "    Time         datetime64[ns] 2005-07-02 <xarray.DataArray (Depth_c: 50)>\n",
      "array([ 21.10233502,  20.96159023,  20.58858233,  20.10892434,\n",
      "        19.67015963,  19.28889264,  18.9548632 ,  18.66008012,\n",
      "        18.39584352,  18.14253007,  17.92917217,  17.72578309,\n",
      "        17.5173952 ,  17.30134111,  17.04102643,  16.74390922,\n",
      "        16.38022699,  15.91659694,  15.32224204,  14.60786933,\n",
      "        13.74052471,  12.67512429,  11.44249457,  10.1875584 ,\n",
      "         8.96519207,   7.83072439,   6.78052544,   5.812746  ,\n",
      "         5.0236021 ,   4.50855976,   4.24120976,   4.10832433,\n",
      "         4.01202286,   3.90748005,   3.77673659,   3.63052264,\n",
      "         3.49743043,   3.38338822,   3.25466227,   3.0790568 ,\n",
      "         2.85941375,   2.62189665,   2.38680162,   2.16975342,\n",
      "         2.0088036 ,   1.92156767,   1.8580864 ,   1.81438341,\n",
      "                nan,          nan])\n",
      "Coordinates:\n",
      "    Longitude_t  float32 299.5\n",
      "  * Depth_c      (Depth_c) float32 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Latitude_t   float32 39.5\n",
      "    Time         datetime64[ns] 2005-07-02\n",
      "<xarray.DataArray 'u' (Depth_c: 50)>\n",
      "array([ 0.23944686,  0.202685  ,  0.18200348,  0.17409228,  0.16885349,\n",
      "        0.16435893,  0.16032954,  0.15664397,  0.15308236,  0.14954343,\n",
      "        0.14679012,  0.14432698,  0.1414368 ,  0.13848211,  0.13460122,\n",
      "        0.13018939,  0.1244467 ,  0.11745287,  0.10890713,  0.09883665,\n",
      "        0.08717503,  0.07444824,  0.06123254,  0.04839063,  0.03677421,\n",
      "        0.0268672 ,  0.01867473,  0.0121845 ,  0.00760518,  0.00489767,\n",
      "        0.00353413,  0.00285542,  0.0024165 ,  0.00199221,  0.00144401,\n",
      "        0.0006767 , -0.00030298, -0.00136913, -0.00236471, -0.00318346,\n",
      "       -0.00376874, -0.00404952, -0.00395442, -0.00351102, -0.00283759,\n",
      "       -0.00198498, -0.00068925,  0.00059345,         nan,         nan])\n",
      "Coordinates:\n",
      "    Longitude_t  float32 299.5\n",
      "  * Depth_c      (Depth_c) float32 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Latitude_t   float32 39.5\n",
      "    Time         datetime64[ns] 2005-07-02 <xarray.DataArray 'v' (Depth_c: 50)>\n",
      "array([ 0.03967041,  0.03510151,  0.04019022,  0.04668061,  0.04913285,\n",
      "        0.0502381 ,  0.05102414,  0.0515379 ,  0.05193662,  0.05222316,\n",
      "        0.05213001,  0.05184483,  0.05137736,  0.05068032,  0.04974374,\n",
      "        0.04858968,  0.04708494,  0.04507946,  0.04265563,  0.03937409,\n",
      "        0.03542616,  0.03087777,  0.02564656,  0.02021027,  0.0150082 ,\n",
      "        0.01028549,  0.00620139,  0.00287003,  0.00046666, -0.00091146,\n",
      "       -0.00144855, -0.00152616, -0.00147558, -0.00145375, -0.00149181,\n",
      "       -0.00157445, -0.00168814, -0.0018392 , -0.00204265, -0.00229426,\n",
      "       -0.00256875, -0.0028379 , -0.00305751, -0.0031456 , -0.00304893,\n",
      "       -0.00274524, -0.00244838,         nan,         nan,         nan])\n",
      "Coordinates:\n",
      "    Longitude_t  float32 299.5\n",
      "  * Depth_c      (Depth_c) float32 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Latitude_t   float32 39.5\n",
      "    Time         datetime64[ns] 2005-07-02 <xarray.DataArray (Depth_c: 49)>\n",
      "array([  4.49013733e-05,   1.32619531e-04,   1.67213349e-04,\n",
      "         1.39980381e-04,   1.08620176e-04,   8.89353316e-05,\n",
      "         7.59696940e-05,   6.85660114e-05,   6.13053056e-05,\n",
      "         4.96623307e-05,   4.26153730e-05,   3.83933836e-05,\n",
      "         3.33244718e-05,   3.14573673e-05,   2.74780120e-05,\n",
      "         2.50869570e-05,   2.35907064e-05,   2.26047540e-05,\n",
      "         2.06309696e-05,   1.93499569e-05,   1.85802260e-05,\n",
      "         1.73887368e-05,   1.49995125e-05,   1.25945779e-05,\n",
      "         1.06210281e-05,   9.69791903e-06,   9.18845407e-06,\n",
      "         7.56655382e-06,   5.00383224e-06,   2.83633215e-06,\n",
      "         1.67170878e-06,   1.19350982e-06,   9.87252978e-07,\n",
      "         9.54096555e-07,   1.05979705e-06,   1.18957478e-06,\n",
      "         1.23432375e-06,   1.22190029e-06,   1.20084648e-06,\n",
      "         1.18287498e-06,   1.14801849e-06,   1.08643614e-06,\n",
      "         9.36511867e-07,   6.17342678e-07,   3.35507108e-07,\n",
      "         3.19646379e-07,   2.60733349e-07,              nan,\n",
      "                    nan])\n",
      "Coordinates:\n",
      "    Longitude_t  float32 299.5\n",
      "  * Depth_c      (Depth_c) float32 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Latitude_t   float32 39.5\n",
      "    Time         datetime64[ns] 2005-07-02\n"
     ]
    }
   ],
   "source": [
    "print absS_GS, consT_GS\n",
    "print u_GS, v_GS, N2_GS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model_func(x, A, K, C):\n",
    "    return A * np.exp(K * x) + C\n",
    "\n",
    "def fit_exp_linear(x, y, C=0):\n",
    "    \"\"\"fit an exponential\"\"\"\n",
    "    y = y - C\n",
    "    y = np.log(y)\n",
    "    K, A_log = np.polyfit(x, y, 1)\n",
    "    A = np.exp(A_log)\n",
    "    return A, K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "u0 = u_GS.values[-3]; v0 = v_GS.values[-3]; N20 = N2_GS.values[-3]\n",
    "\n",
    "# u_raw = u_GS.values.copy()\n",
    "# v_raw = v_GS.values.copy()\n",
    "# u_raw[u_raw<0.] = 1e-3\n",
    "# v_raw[v_raw<0.] = 1e-3\n",
    "\n",
    "A, K = fit_exp_linear(z_t.values[:-3], u_GS.values[:-3], C=-5e-3)\n",
    "u_fit = model_func(z_t.values, A, K, -5e-3)\n",
    "A, K = fit_exp_linear(z_t.values[:-3], v_GS.values[:-3], C=-5e-3)\n",
    "v_fit = model_func(z_t.values, A, K, -5e-3)\n",
    "A, K = fit_exp_linear(-zN2_GS.values[:-3], N2_GS.values[:-3], C=N20)\n",
    "N2_fit = model_func(-zN2_GS.values, A, K, N20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'u' (Depth_c: 50)>\n",
      "array([ 0.23944686,  0.202685  ,  0.18200348,  0.17409228,  0.16885349,\n",
      "        0.16435893,  0.16032954,  0.15664397,  0.15308236,  0.14954343,\n",
      "        0.14679012,  0.14432698,  0.1414368 ,  0.13848211,  0.13460122,\n",
      "        0.13018939,  0.1244467 ,  0.11745287,  0.10890713,  0.09883665,\n",
      "        0.08717503,  0.07444824,  0.06123254,  0.04839063,  0.03677421,\n",
      "        0.0268672 ,  0.01867473,  0.0121845 ,  0.00760518,  0.00489767,\n",
      "        0.00353413,  0.00285542,  0.0024165 ,  0.00199221,  0.00144401,\n",
      "        0.0006767 , -0.00030298, -0.00136913, -0.00236471, -0.00318346,\n",
      "       -0.00376874, -0.00404952, -0.00395442, -0.00351102, -0.00283759,\n",
      "       -0.00198498, -0.00068925,  0.00059345,         nan,         nan])\n",
      "Coordinates:\n",
      "    Longitude_u  float32 299.0\n",
      "  * Depth_c      (Depth_c) float32 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Latitude_t   float32 39.5\n",
      "    Time         datetime64[ns] 2005-07-02\n",
      "Attributes:\n",
      "    units: m/s             \n",
      "    long_name: Zonal Velocity                                                          \n",
      "    grid: UM <xarray.DataArray 'v' (Depth_c: 50)>\n",
      "array([ 0.03967041,  0.03510151,  0.04019022,  0.04668061,  0.04913285,\n",
      "        0.0502381 ,  0.05102414,  0.0515379 ,  0.05193662,  0.05222316,\n",
      "        0.05213001,  0.05184483,  0.05137736,  0.05068032,  0.04974374,\n",
      "        0.04858968,  0.04708494,  0.04507946,  0.04265563,  0.03937409,\n",
      "        0.03542616,  0.03087777,  0.02564656,  0.02021027,  0.0150082 ,\n",
      "        0.01028549,  0.00620139,  0.00287003,  0.00046666, -0.00091146,\n",
      "       -0.00144855, -0.00152616, -0.00147558, -0.00145375, -0.00149181,\n",
      "       -0.00157445, -0.00168814, -0.0018392 , -0.00204265, -0.00229426,\n",
      "       -0.00256875, -0.0028379 , -0.00305751, -0.0031456 , -0.00304893,\n",
      "       -0.00274524, -0.00244838,         nan,         nan,         nan])\n",
      "Coordinates:\n",
      "    Longitude_t  float32 299.5\n",
      "  * Depth_c      (Depth_c) float32 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Latitude_v   float32 39.0\n",
      "    Time         datetime64[ns] 2005-07-02\n",
      "Attributes:\n",
      "    units: m/s             \n",
      "    long_name: Meridional Velocity                                                     \n",
      "    grid: VM\n",
      "<xarray.DataArray 'u' (Depth_c: 50)>\n",
      "array([ 0.24281602,  0.20863804,  0.18932983,  0.18118398,  0.17612447,\n",
      "        0.17162971,  0.16771592,  0.16384149,  0.16035922,  0.15705316,\n",
      "        0.1543837 ,  0.15169521,  0.14882469,  0.14594722,  0.14190947,\n",
      "        0.13762632,  0.13213648,  0.12526578,  0.1166122 ,  0.10635603,\n",
      "        0.09433772,  0.08094706,  0.06682488,  0.05285184,  0.04002678,\n",
      "        0.02895699,  0.01978298,  0.01256612,  0.00750119,  0.004512  ,\n",
      "        0.00304223,  0.0023826 ,  0.00201441,  0.00166243,  0.0011796 ,\n",
      "        0.00048553, -0.00039988, -0.00135567, -0.00224543, -0.00298431,\n",
      "       -0.00353272, -0.00382951, -0.00377605, -0.00336971, -0.00271207,\n",
      "       -0.00179736, -0.00051487,  0.0006103 ,         nan,         nan])\n",
      "Coordinates:\n",
      "    Longitude_u  float32 300.0\n",
      "  * Depth_c      (Depth_c) float32 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Latitude_t   float32 39.5\n",
      "    Time         datetime64[ns] 2005-07-02\n",
      "Attributes:\n",
      "    units: m/s             \n",
      "    long_name: Zonal Velocity                                                          \n",
      "    grid: UM <xarray.DataArray 'v' (Depth_c: 50)>\n",
      "array([ 0.03498516,  0.03102324,  0.03670016,  0.04119537,  0.04248637,\n",
      "        0.04322845,  0.0436029 ,  0.04383555,  0.0438052 ,  0.043598  ,\n",
      "        0.04321078,  0.04266092,  0.04194829,  0.04106502,  0.03984407,\n",
      "        0.03842646,  0.03672103,  0.03459246,  0.03208185,  0.02906089,\n",
      "        0.02571714,  0.02202296,  0.01798431,  0.01410491,  0.01057055,\n",
      "        0.00744473,  0.00468729,  0.00233127,  0.00059059, -0.00039309,\n",
      "       -0.00077711, -0.00087736, -0.00092168, -0.00099712, -0.0011234 ,\n",
      "       -0.00130368, -0.00154013, -0.00183272, -0.00217052, -0.00252132,\n",
      "       -0.00283725, -0.00308227, -0.00323668, -0.00326342, -0.00314228,\n",
      "       -0.00293316, -0.00262752,         nan,         nan,         nan])\n",
      "Coordinates:\n",
      "    Longitude_t  float32 299.5\n",
      "  * Depth_c      (Depth_c) float32 5.0 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Latitude_v   float32 40.0\n",
      "    Time         datetime64[ns] 2005-07-02\n",
      "Attributes:\n",
      "    units: m/s             \n",
      "    long_name: Meridional Velocity                                                     \n",
      "    grid: VM\n"
     ]
    }
   ],
   "source": [
    "print u.sel(Longitude_u=GS[0]-.5, Latitude_t=GS[1]), v.sel(Longitude_t=GS[0], Latitude_v=GS[1]-.5)\n",
    "print u.sel(Longitude_u=GS[0]+.5, Latitude_t=GS[1]), v.sel(Longitude_t=GS[0], Latitude_v=GS[1]+.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XXnuN2NhYPwR6jqyFJUvgvfecnjHu7+qqVeGGG+Cmm5zbq4vpx5zc3FwSEhI8\nhUD3smnTJtLS0gp8T1RUFO3bt/eMEeh+Xr9+fd0WLEGhHOWrJOeoPXv2cO+99zJz5kzAmXxo+PDh\nvPDCCzTN3zM6MREeftgZZgGgbl2nx+Ctt4J+eBCRUkAFQj8qyclNpLTIyclh586dbNiwgbVr17Jm\nzRpWr17N3r17ffYNDw8nOjqarl270r17d3r06EHHjh1VNCzn1PjypfxUzG67DT74ALp2dXqMnOPE\nFMeOHeO6667jwQcfZODAgcEvZB065Fzfu+86txO79evnXPuwYeDHWXmttezbt8/zw9HGjRs9PQML\nmhUUnMkAYmJiPIu7IFi7dm2/xSXiD8pRvkpqjlq7di1XXXUViYmJVKpUidtvv50HH3yQ5s2b590x\nOxveessZV/D4cahYER54AMaNg8jI4AQvInIWVCD0o5Ka3ETKgoMHD7JmzZo8y7Zt28j//1yFChXo\n2LEjPXr08Czt27cnNDQ0SJFLoKnx5Uv5qZht2eJMSvKf/0CbNkV+W3Z2NqGhocEvABbEWqfY+fbb\n8L//ObMSgzPByC23OD1iWrU659McOXLEpyf5xo0bOeI9wYmXunXr5ikEuouBtWrVOudYRAJBOcpX\nScxRc+fO5frrryclJYU+ffrwxRdfULduXd8dly51vv83bHBeX3ut04u8WbOAxisi4g8qEPpRSUxu\nImVZSkoK69atY9WqVfz666/8+uuvbNmyxWe/iIgIunbt6ikYXnDBBTRr1qxkNsrlnKnx5Uv5qWTJ\nycnh888/56mnnuKVV15hyJAhwQ7ppJQU+OQTpzC4fr2zzhi48kq4+24YOPCsbpU7ceIEcXFxeYqA\nGzZsIDExscD9a9as6ZnEKiYmxvOoHoFS2ilH+SppOSoxMZG2bduSkpLC6NGjee+993zvTklJgcce\ng3/9y/lBpXlzpxfhVVcFJ2gRET9QgdCPSlpyEymPjh07xurVqz0Fw19//ZVd3rNqukRFRdGzZ0/P\n0qNHD81QWUao8eVL+alksNYyY8YMxo8fT1xcHABDhw5l+vTpQY4MSEhwGrdTpjhjKgKcdx7cfjuM\nGeM0fovowIEDrF27lnXr1nmWzZs3k5OT47Nv5cqVPQVA71ntNUaglFXKUb5KWo666aab+Pjjjxky\nZAhff/2173fRvHlwxx3OuLOhofDoo85sxZUrByVeERF/UYHQj0pachMRR1JSEitXrvQUDFesWMGh\nQ4fy7BOlY9CsAAAgAElEQVQSEkJsbKynYHjhhRfSpk0bzZxcCqnx5Uv5Kfh27tzJ9ddfz+rVqwFo\n0qQJ48eP56abbiI8PDx4gS1b5twON306uAt4F18M997rjC14ijFds7Oz2bp1a55i4Nq1azlw4IDP\nviEhIbRt25YOHTp4ltjYWJo3b67vWSlXlKN8laQctXDhQvr160fFihWJj4+nRYsWJzempcFDDzk9\nrMGZnOr996FLl+AEKyLiZyoQ+lFJSm4iUjhrLb/99hvLly/3LGvXriU7OzvPfjVq1OCCCy6gZ8+e\n9OrVi549e6qXYSmgxpcv5Sc/O3ECKlU6o7dkZGTQunVrsrOz+cc//sHtt98evAmVcnLgq6/gtddg\nxQpnXVgY3HgjjB0L3bv7vCU9PZ1169axcuVK1q5dy9q1a4mLi+PEiRM++0ZGRtKpUyc6depE586d\n6dSpEzExMVSpUqW4r0ykxFOO8lVSctS+ffvo2rUr+/fvZ/z48Tz11FMnN65bByNHwqZNEB7uTEjy\n8MPOcxGRMkIFQj8qKclNRM5ceno6q1at8hQMly1b5jM2VmhoKN26daN379707t2bXr16aTysEkiN\nL1/KT352442Qmwv//jdERRX5bevXr6dVq1bBK5RlZjrjC774Imzb5qyrVQvuusvpMdiwIeAUMzds\n2MDKlSs9y8aNGwu8RbhZs2Z5CoGdOnWiWbNm6hUoUgjlKF8lIUdlZmZy6aWXsnTpUvr168cPP/xA\nWFiYM77gm286xcDMTGjXDj77DDp1Cmq8IiLFQQVCPyoJyU1E/GfPnj0sX76cn3/+mcWLF7NmzRqf\nBnJMTAx9+vTxFA0bNWoUpGjFTY0vX8pPfjRnjjNhR5UqEB8PTZvm2bxy5UqOHDnCFVdcEaQAC5CW\nBu++Cy+/DHv2OOuaN4e//52sUaOI27kzTzFw/fr1ZGVl5TlESEgI0dHRdO/enS5dutC5c2c6duxI\njRo1gnBBIqWXcpSvYOeo1NRUxowZw7Rp02jYsCGrV68mKioKjh6FP/0JZs1ydrzrLqfntXpDi0gZ\npQKhHwU7uYlI8Tp+/DjLli1j8eLFLFq0iBUrVpCRkZFnn+bNm9O7d2/69OnDZZddRrNmzYITbDmm\nxpcv5Sc/SU+HmBjYudMptj30kGfT+vXrGT9+PF9//TXNmjVjy5YtVKhQIYjBAsnJMGmSM8bgwYMA\nZLRsyYrLLuPL0FB+Wb2atWvX+nyPGWNo164d3bt3p3v37nTr1o3OnTsTERERjKsQKVOUo3wFM0et\nWrWKUaNGsXXrVipVqsSCBQu44IILYONGGDoUtm+HmjXhvfec1yIiZZgKhH6kBphI+ZKRkcGvv/7q\nKRguXbqU48eP59mnXbt2DBo0iEGDBtGnT5/gjTlWjqjx5Uv5yU+efBKefho6doSVKyE8nN27d/PE\nE0/w0UcfYa2lcuXK3HfffYwfPz54Y5amp8OkSdgXX8QcPgzAturVeTo3l6nHj5P/X0Lr1q09xUB3\nD8HIyMjAxy1SDihH+QpGjsrNzeWVV17hH//4B1lZWcTExPDpp5/SoUMHZ4zWm2+G1FRnIpIZM0A/\n+IpIOaACoR+pASZSvuXk5LBu3ToWL17MwoULmTdvHsnJyZ7tERERXHrppZ6CoXoXFg81vnwpP/nB\nrl3O2FMnTsDixdCrF9ZaunfvzurVqwkPD+eee+5h3Lhx1KtXLyghHt6/n71PP03Tjz+mekoKAIuB\np4EfXfs0aNCAiy66iB49etC9e3e6du2q24RFAkg5ylegc1RiYiJ/+tOfmD9/PgD3338/EydOpHLF\nivDEE/D8886OI0c6wzPolmIRKSdUIPQjNcBExFtWVhY///wzc+bMYc6cOaxfvz7P9vbt2zNo0CCu\nvPJKevXqpd6FfqLGly/lJz84fhyeew4OHIAPPvCs/uabb/jkk094/vnnadmyZcDCcc/GvnTpUpYu\nWkSNOXMYk5iIO4LVwGPA3pgYevXuzcUXX0yvXr1o2rQpxuh/D5FgUY7yFcgctWDBAoYPH05SUhJR\nUVF88MEHXHnllc4EJLfcAp9+CiEhzjASY8eCvi9FpBxRgdCP1AATkVPZs2cP3333HXPmzOGHH37I\ncztyREQE/fv3Z8SIEQwZMoTKlSsHMdLSTY0vX8pPfmRt0BqMe/fuZe7cucydO5dFixaxf/9+BgAv\nAx1c++yqXJmlAwdS47bbuPDii6lZs2ZQYhWRgilH+QpEjrLW8uqrr/Loo4+Sk5PDpZdeyrRp06hb\nt67zA9CwYfDjj1C1qnOLcf/+xRqPiEhJpAKhH6kBJiJFlZmZ6eldOHv2bDZu3OjZFhkZybBhwxg9\nejSXXHIJYWFhQYy09FHjy5fy07nZt28fzz33HBMnTgzoRB0ZGRksXbqU7777ju+++44NGzZ4trUG\n/hUeTn/XbMMZ9eoR+swzhN1yC+g7Q6TEUo7yVdw56vjx49x666189dVXADz66KM888wzzt9XBw44\nM9OvXg1RUc5M9V27FlssIiIlmQqEfqQGmIicrd27d/PVV18xdepUVq5c6Vlft25dRowYwejRo+ne\nvbtuDSwCNb58KT+dndzcXCZPnsyjjz5KcnIyjzzyCC+++GKxnnPHjh2eguD8+fNJTU31bIuIiGBw\n7948kplJ58WLMVlZEBkJ48fD/feDhikQKfGUo3wVZ45KSEhgwIABbN68mWrVqvHf//6Xa6+91tm4\naxdcdhns2AEtW8Lcuc6jiEg5pQKhH6kBJiL+sHXrVqZNm8bUqVPZvn27Z33r1q0ZPXo0o0aNonXr\n1kGMsGRT48uX8tOZ27BhA2PGjGH58uUAXHXVVUyaNImmTZv69TypqaksWLDAUxT0/n8eoGPHjgwY\nMICBV1xB7x07CH/ySUhKcm5zvu02Z1zEunX9GpOIFB/lKF/FlaP27t1L37592bFjB7GxsUyfPv3k\n30979kDfvvDbb9CtG8ye7fQgFBEpx1Qg9CM1wETEn6y1rFy5kqlTp/LZZ59x4MABz7aePXvy17/+\nleuvv57w8PAgRlnyqPHlS/npzKxfv55u3boxKDubKrVqccM77zBs2DC/9eBNSUnh66+/Ztq0afz4\n449kZmZ6ttWsWZMrrriCgQMH0r9/fxo2bAhr1sCdd8KqVc5OF18M//yn06gVkVJFOcpXceSo/fv3\n07dvX7Zu3Uq3bt348ccfT87YnpgI/frBtm3Qvbsz9mD16n49v4hIaaQCoR+pASYixSU7O5v58+cz\ndepUpk+f7pngpGHDhtx7772MGTOG2rVrBznKkkGNL1/KT2fGWsvtf/4zr3z9NbVSUuD77+GKK87p\nmJmZmXz33XdMmzaNmTNnkp6eDjj/Xs8//3wGDhzIgAED6NGjx8lxR9PT4amn4JVXICcHGjVyZtYc\nPlwza4qUUspRvvydo5KSkrjkkkuIj4+nU6dO/PTTT9SqVcvZeOCAUxzcvBm6dIF580CTOYmIACoQ\n+pUaYCISCGlpaUydOpU33niD+Ph4ACpXrsyf//xn/va3vxEdHR3kCINLjS9fyk9nzk6ZghkzBtq3\nhw0bIDT0jI+Rm5vL4sWLmTZ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88cUXDB8+nHr16rFt2zaqBul2HzW+fJWH/DR48GBmzZrF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\nJEmSJElSiZkglCRJkiRJkkrs/wcbX1wrp4esdAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f986ab739d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,5))\n",
    "\n",
    "ax1 = fig.add_subplot(131)\n",
    "ax1.plot(potrho_GS.values, -z_t.values, 'k', lw=2)\n",
    "ax1.set_title('Potential density', fontsize=14, y=1.03)\n",
    "# ax1.set_xlabel('lon', fontsize=12)\n",
    "# ax1.set_ylabel('lat', fontsize=12)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "ax2 = fig.add_subplot(132)\n",
    "ax2.plot(u_GS.values, -z_t.values, 'k', lw=2, label=r'$u$')\n",
    "ax2.plot(v_GS.values, -z_t.values, 'k--', lw=2, label=r'$v$')\n",
    "ax2.plot(u_fit, -z_t.values, 'r', lw=2)\n",
    "ax2.plot(v_fit, -z_t.values, 'r--', lw=2)\n",
    "ax2.set_title('Horizontal velocities', fontsize=14, y=1.03)\n",
    "# ax2.set_xlabel('lon', fontsize=12)\n",
    "# ax2.set_ylabel('lat',fontsize=12)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.legend(loc='lower right', fontsize=14)\n",
    "\n",
    "ax3 = fig.add_subplot(133)\n",
    "ax3.plot(N2_GS.values, zN2_GS.values, 'k', lw=2)\n",
    "ax3.plot(N2_fit, zN2_GS.values, 'r', lw=2)\n",
    "ax3.set_title(r'$N^2$', fontsize=16, y=1.03)\n",
    "# ax1.set_xlabel('lon', fontsize=12)\n",
    "# ax1.set_ylabel('lat', fontsize=12)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zphi, Rd_GS, vd = baroclinic.neutral_modes_from_N2_profile(-zN2_GS.values, \n",
    "                                                        N2_GS.values, f0_meta.sel(Latitude_t=GS[1]).values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m\u001b[0m",
      "\u001b[1;31mNameError\u001b[0mTraceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-76c8ba7b8c0f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m np.savez('OCCA_global',\n\u001b[0m\u001b[0;32m      2\u001b[0m         \u001b[0mabsolute_salinity\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mabsS\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconservative_temperature\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconsT\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m         \u001b[0mpotential_density\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpotrho_meta\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m         \u001b[0mz_N2\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mzN2_meta\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mN2\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mN2_meta\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m         \u001b[0mu_at_Tpoints\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mu_coinT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv_at_Tpoints\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mv_coinT\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "np.savez('OCCA_global',\n",
    "        absolute_salinity=absS, conservative_temperature=consT,\n",
    "        potential_density=potrho_meta, \n",
    "        z_N2=zN2_meta, N2=N2_meta,\n",
    "        u_at_Tpoints=u_coinT, v_at_Tpoints=v_coinT\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  2.02595962e+11   2.57873859e+04   1.39004971e+04   9.60806512e+03\n",
      "   6.90666297e+03   5.87640200e+03] (48,) [-0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757\n",
      " -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757\n",
      " -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757\n",
      " -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757\n",
      " -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757\n",
      " -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757\n",
      " -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757\n",
      " -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757 -0.14433757] [ 0.2287108   0.22844163  0.22738221  0.22538205  0.22315635  0.22100572\n",
      "  0.21890111  0.21681132  0.21466035  0.21249153  0.21051281  0.20857709\n",
      "  0.20653185  0.20436013  0.20172795  0.19864415  0.19475028  0.18960952\n",
      "  0.18268901  0.17391394  0.16270843  0.14845062  0.13133273  0.11305419\n",
      "  0.09473064  0.07691331  0.05874788  0.04009015  0.02387257  0.01278873\n",
      "  0.00637754  0.00253038 -0.00029387 -0.00275014 -0.00532013 -0.00849032\n",
      " -0.01248929 -0.01713441 -0.02220176 -0.02756655 -0.0331077  -0.03857259\n",
      " -0.04363333 -0.04770119 -0.05004272 -0.05105026 -0.05168191 -0.05185931]\n"
     ]
    },
    {
     "data": {
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5UdaP+OVxv+yx7l6nk98XFPBxbS13JCdzc3IyoeahmfpaUvIkhYV/ZsqUjwkL\nmz4kNigGDiUWimHLQXtwP/IIrF4NS5YMmg3NW5upeKWCitcqCJsVRvqf03FkD+zY/JOrn+Q/O/7D\n8quWH/Ksp9yWFn5XUMDm5ma+mDaN5KChWcFdXv4K+/bdz5w5uUPSvmLgUGKhGLYcJBaNjb4Feps3\nQ/LgBsfTXTqlT5ey7y/7iDkrhrTfpxGUGvgXsiENsp7M4oUfvsAJYw7fP/PQvn08XVrKF9OmkR48\n+A77xsY15OX9jFmz1g1624qBpT9iMTwD+CuOXsLD4cor4emnB71pc5CZlNtTOCbvGGyjbaydsZZd\nd+zCW+MNaDvLdi8jxBrC8anH9+n+O1NTuSMlhZM2biSvtTWgth0KHk8pdvuo3isqRhRKLBSDz003\n+ZzdTueQNG+JsDD2T2OZvXU2htPg+wnfU3hfIbpTD8jzH1/9ODfPublfmxPdOHo0i9PSmL9xI1ub\nmwNi16Hidpdis43uvaJiRKHEQjH4ZGbCrFnw1ltDaoY9yU7mU5nkfJdD07om1uWso3FNY7+eWdFc\nwTf7vuGy7Mv6bd+1SUncl57OBdu24dQDI2S9IaWkqupfhIUdPHtLMbJRYqEYGm64YUiGoroiZFwI\n2f/JJm1xGlvO2cLe3+7F8Bh9etZHeR9x+rjTCbYGxtfw46Qkpjsc3FtQEJDn9UZFxetoWj2JiT8Z\nlPYURw5KLBRDw5lnQkUFrF071Ja0E39JPLM2zqJ5YzPr5qyjefPhD/98kPcB52aeG1C7nhg/nlfK\ny/m+sX+9nt7weuvYs+dOMjOfUeE/FAehxEIxNJjN8LOf+fblHkbYk+xkf5BN8q3JbDplE4X3FyKN\nQ5t51+ptZUXBCs4af1ZAbYqz2Xh03Diu3bEDt9G3Hs+hsGfP/yM29keEh88ZsDYURy5KLBRDx6JF\n8O67UF091JZ0QghB0k+SmLluJrVLa9l81mY81Z5e71tZuJJpCdOICo4KuE2XxMczym7n9YqKgD8b\nYN++B2ho+Jr09PsG5PmKIx8lFoqhIy7OF432hReG2pIuCUoNYtoX03BMcbBu5joaV/c8DPRlwZfM\nT5s/ILYIIbg7NZWHioowArzGqKTkSUpLn2PatM+xWiMD+mzF0YMSC8XQcvPNvqEoTRtqS7rEZDWR\n8VAG4x4dx5ZztlDyZAndLQj9suBL5qcPjFgAzI+MJNRk4sOamoA9s6zsn+zb9wDTpn2O3a6myyq6\nR4mFYmjWg5LwAAAgAElEQVTJyYExY+C994bakh6JOz+OGd/MoPS5UnKvyEVr7ixuje5Gtldt59jk\nYwfMBiEEd6Wm8sC+fQF5XkXFm+zdew/Tpi1XgQMVvaLEQjH0/OIX8Je/HBAXZPgRMj6EnO9yMNlN\nrJ+znpbclvZr3+z7hlmjZhFkGdh4Tj+Ki6PI7WZLPxbqGYaHXbt+wZ49v2Lq1E8JCZkQQAsVRytK\nLBRDz3nngdcLH3001Jb0ijnYzMSXJpLyyxQ2nriRijd9Duf/Ff6Pk8acNPDtC8Hl8fF9dnQ7nXvZ\nsOF4nM7dzJq1AYdjSoAtVBytKLFQDD0mEyxe7EvDvHfRRtK1SUxdPpW9v91L3o15fLPrG04cc+Kg\ntH1FQgJvVFYetqO7qurfrF9/DPHxl5Od/R5Wa/QAWag4GlFioRgeLFwIhuHb6+IIIWx6GDPXzqS1\nuJWr/nQVM/QZg9LuVIeDCIuFrxsaDqm+rrvIy7uJ3bvvZMqUj0lJua1fcasUIxMlForhgRDw+9/D\nr3/t2yTpCMEaaaX6kWp2/WAX24/bTsWSgVkHcSCXx8fzVmVlj3WkNKisfIu1a6fh8ZQzc+Z6wsNn\nD4p9iqMPJRaK4cMPfwjjxsF9R9bCsOV7lhP8s2CmfDyFwt8Xsu3SbQEPe34g58TE8GltbZfXpJRU\nV3/A2rUzKCp6hPHjn2Dy5H+pNRSKfqHEQjF8EAKeecaXNmwYamsOmWV7lrEgYwHhs8OZuX4m9lF2\n1kxbQ83SwK2HOJDs0FBchsGuDvtdSCmprV3G+vXHsHfvb0lP/yM5Od8RHX2aGnZS9BsVLUwxvEhK\ngocfhp/8xLf9qs021Bb1SGVLJXvr9jJntC+ekjnYzLhHxhFzbgw7f7KT6gXVZPw1A4sjsF81IQQL\noqP5rK6OcSEh1NevZO/ee/B4KklP/wNxcRciDnE7V4XiUFB/TYrhx1VXwejRvrUXw5wv9nzBSWkn\nYTVbO5VHzYti1qZZSE2ydtpa6lfWB7zt06Oi2Fj5FZs2nc6OHdeQlLSI2bO3Eh9/sRIKRcBRPQvF\n8EMIeO453+ru44+Hk08eaou65bPdn7Fg7IIur1nCLUz8x0SqP6xm+yXbibsgjvQ/p2MJ69/XTtOa\nqKx8i/Syf7CgaTex4/9AUtK1mEzDuxemOLJRPz8Uw5PRo2HJErjsMsjNHWprukQ3dD7J/4RzMs/p\nsV7sD2OZvXU2eqvOmslrqP7w8KPsSilpaPiGHTuu5bvvUqmt/ZiMMfdwq+VfyJgfK6FQDDiqZ6EY\nvsyfDw8+CGefDatWQULCUFvUie9LvifRkciYyDG91rVGW5n4j4nUfVlH3vV5VLxawbjHxmFPtPd4\nn9tdTkXFK5SVvQhAUtIi5sy5H5vN91mMD93ATqeT5KCBDTOiUKiehWJ4c801cOWVvpAgTudQW9OJ\nD3d+2Guv4kCi5kcxa/MsgscFs3bqWkpfKD0oiq1heKmu/oAtW85jzZosWlt3MnHii8yZk0tq6p3t\nQgEwMSSEnR1mRCkUA4XoLtzycEYIIY9Eu0ciQgQggoeUPqe3ywVvv+0LDzIMyH4qm+d/+DxzU+b2\n6f7mzc3s/L+dmEJMTHhuAiQXUVb2IhUVrxAUNJakpEXExV2MxeLo9hmPFBVR4HLx2Pjxff1nKEYQ\nQgiklH2aR92vb50Q4kEhRK4QYqMQ4t9CiPAO1+4WQuT7ry/oUJ4jhNgshMgTQjzaodwmhFjiv2eV\nECK1P7YpjiKEgH/8A6qq4M47h0X8qL11e6lsqWyfMtsXHFMdTPkqDduiz1n9xbGs+/p4pGEwbdp/\nycn5hqSka3sUCoAJqmehGCT6+xNtGTBZSjkdyAfuBhBCTAIuBrKAM4GnxP5VQU8Di6SUmUCmEOJ0\nf/kioFZKOR54FHiwn7Ypjibsdt8WrF98AXfdNeSC8WGebwjKbDIf9r2a1kh5+ats3nw2368Zi5zx\nPZkn/Yawvy6l8erLEGW9+0DaiLZYaNT1w7ZBoThc+iUWUsrPpZRtO8h/ByT7j88FlkgpNSllAT4h\nmSOESATCpJRr/PVeARb6j88DXvYfvwOc0h/bFEch0dHw3//60u23D6lgvL/zfc6bcN4h19f1Fior\n32Lr1vNZtSqFqqp/kZBwOXPnFpOd/Q6jJl3MtKUzib88ng1zN1D63MG+jK5wGgbBw2RYTnF0E8jZ\nUNcCb/qPRwOrOlwr8ZdpQHGH8mJ/eds9RQBSSl0IUS+EiJZSdh0ARzEyiY6Gzz+HM86AG2+EJ54Y\ndB9GnbOONSVrOHXsqT3W03UXtbVLqax8i9rapYSHzyU+/lImTHipyzhNwiRIvjmZqFOiyL0ql+oP\nqpnwwoQeZ0y5DIMgJRaKQaBXsRBCLAc6zlkUgATukVJ+6K9zD+CVUr7ZxSP6So9OmMWLF7cfz5s3\nj3nz5gWwacWwJjISli2DM8+En/4Unn12UAVj6a6lzEubR6gt9KBrhuGhrm45lZVvUVPzIQ7HDOLj\nL2X8+Cew2WIP6fmhk0LJWZVDwR8KWDt9LZlPZxJ3flyXdZVYKHpixYoVrFixIiDP6vdsKCHEj4Hr\ngJOllG5/2a8AKaV8wH/+KXAvUAh8KaXM8pdfCpwkpfx5Wx0p5fdCCDNQJqWM76ZNNRvqCCEgs6G6\no7nZtwYjPd3nADcfvv+gL1zyziUsGLuARTmLADAMjfr6L6msfIvq6vcICZlIfPylxMVdiN2e2K+2\nGr5tIPeqXCJPimTco+OwhHf+ffdGRQUf1dTwxqRJ/WpHMTIYytlQZwB3Aue2CYWfD4BL/TOc0oFx\nwGopZTnQIISY43d4Xw283+Gea/zHFwH/7Y9tihGAwwGffALFxXD11aBpA96kR/ewbPcyzh5/FvX1\nX5GXdwOrVo1m7957CA2dxKxZG8jJ+Zrk5Jv6LRQAEcdFMGvTLIRFdBljSvUsFINFv3oWQoh8wAa0\nxWL+Tkp5g//a3fhmOHmBW6WUy/zlM4F/AkHAJ1LKW/3lduBVYIb/eZf6neNdtat6FkcIA9qzaMPp\nhAsu8PUsliyB0IOHhwLF8tzn+HbHYhYkWrBYooiPv4z4+IsJDh47YG22Uf1hNTuv20nGwxkkXukT\novsLC6nxenl43LgBb19x5NOfnoValKcYUAZFLAC8Xp//YutW+OgjiO9yBLNPuFxFVFa+QUXF61Q1\nFVBrnsHCmU/icGQHrI1DpWV7C5tP30zKnSkk35LMmZs3c31SEufHde3TUCg6MmTDUArFsMFq9fkt\nzjwTjjsO8vP79Tivt57S0hfYsGEea9dOx+ncw7hxj3PL1limTnx8SIQCfM7v6SunU/J4CXsW7+Xb\n+npOiIgYElsUIwsVSFBx9NC2j3dKCpx4om8R37HHHvLthuGmpuZjKipep67uc6KiTiM5+TZiYs7E\nZLKzo3oHbt3LlPgpA/iP6J3gtGBmfD2D707bwO07TMQcb+39JoWinyixUBx9/N//wahRvj29X3jB\nF4SwG6Q0qK//isrK16mq+g8OxzQSEq5gwoR/HLQW4qO8jzhn/DnDYotSW4KNda/Hk/3jMnZcs4MJ\nL07AZFUDBYqBQ/11KY5OzjoLli6Fn/8cnnzyoMvNzVvYvfsuvvsujV27biU4OJNZszYxffp/SUpa\n1OWiuY/yPjrsKLMDyX9pxvj3WLw1XrZdsA3dqcJ+KAYO5eBWDCiD5uDujj17fH6MhQtx3XsDldVL\nqKh4HU2rJz7+chISrsDh6H1Yqc5Zx5hHx1DxywqCrcGDYHjP6FIS+8035M6eTbywsuOaHbjL3Ez5\nYMpBazEUijaUg1uh6I6xY2lY/je2jnmBtV9l4mzcwfjxj3PssQVkZPzlkIQCYNnuZZw45sRhIRQA\n3zQ0MMpmI9Fux2QzkfVaFiETQth++fahNk1xlKLEQnFUIqVOVdW7rF9/HLmlNxN11m+Z+68LmHD1\nJiIb0xHi8P70P939KWeOO3OArD18Hi4q4qbRo9vPhVkw/vHxtOa2UvdF3RBapjhaUWKhOKrQdScl\nJc+wenUW+/bdT3Ly7RxzTB6j027D/MLrcPnlvhlS3357yM+UUvLprk85Y9wZA2j5obO9pYXVjY38\nOLHzCnGTzcTY+8ey+87dSEMN0yoCixILxVGBx1NNQcHv+e67NGprP2bChBfIyfme+PiL8IUaw+dA\n+eUvfTOkFi6Ef/7zkJ69uWIzDpuDjOiMgfsHHAYPFRVxc3IywV3Ewoq7KA5hE1S8XjEElimOZpQn\nTHFE43TupqjoESor3yA29gKmT19BaGhWzzeddRb8739w7rmweTM8+CBYuv8qfLb7M07POL3b64NJ\nscvF+9XV7DrmmC6vCyHIeDiD3MtzibswDnPw4ARXVBz9qJ6F4oiksfF7tm69kHXrjsFiiWT27O1M\nnPhC70LRRlYWfP89bNkC55wD9fXdVh1OQ1B/Ky7mx4mJRFu7X4gXeXwkYTPDKHmsZBAtUxztKLFQ\nHDFIaVBd/QEbNpzItm2XEBl5AsceW8DYsfdhtycd/gOjo31rMTIzfX6MvLyDqjS5m1hTuoZ5afP6\n/w/oJ3VeLy+Vl3N7cnKvdcf+ZSz7HtqHp9ozCJYpRgJqGEox7NF1FxUVr1Fc/FdMphBSUu4kLu5C\nTKYA/PlaLPDYY/D883D88fDaa7BgQfvlLwu+5JjRx+CwOfrfVj95qrSUc2NiSAkK6rVuyIQQ4i+J\np/CPhYz/+/hBsE5xtKN6FophjdO5h7Vrp1Fd/W/Gj3+SmTPXkpBwaWCEoiPXXQf//jdccw089FD7\nSsLPdg0Pf0WVx8NjxcXcmZp6yPek3ZtG6TOlGJoxgJYpRgpKLBTDlsbGtWzYcDzJybcxdepSoqJO\nHti4TCec4PNjvP02XHIJNDfz+d7Pe91rezC4MT+fqxMTmXwYe3WY7CaEVWCyqK+5ov+ovyLFsKSm\nZilbtpxJZubTjB7988FrODUVVq6EsDC8c2YSsa+SaYnTBq/9LvhXZSWbm5v5Q1raYd3nqfBgS7AN\njFGKEYcSC8Wwo6zsRXbs+AnZ2R8QG9t9xNgBIygIXniBNRccx/JnnZg++njwbfBT5fFwy65d/HPi\nxC7XVfSEEgtFIFFioRg2SCkpKPgDhYV/YsaMr4iImDt0xgjBMzk6Xzx6C9xwA9x7LxiDP/Z/Y34+\nVyYkcGwfNjhSYqEIJEosFMMCw9DIy/sp1dUfMGPGt4SEZA6pPVJKvtj7BVPOvQ7WroUVK3z7Y9QN\nXtylvg4/teGt8GJNUBsjKQKDEgvFkKPrLWzduhC3u4jp01dgtyf2ftMAs7NmJ2ZhZlz0OEhIgM8/\n963HmD3bt5BvgOnP8FMbqmehCCRKLBRDisdTycaN87HZ4sjO/gCLZejXMwB8secLThl7yv7ZV1Yr\n/O1vvm1bTz4ZliwZ0Pb7M/zUhqfCgy1RiYUiMCixUAwZra27WL/+OKKjz2DChBcxmYbPkMkXe7/g\nlPRTDr5wxRW+XsY998Add4CmBbzt/g4/taF6FopAosRCMWQUFT1AbOx5pKf/YVjsa92GIQ1WFKzg\n5PSTu64wbRqsWQPbt8Npp0FVVcDa3u10clN+fr+GnwDc5W4av2skZGJIwGxTjGyUWCiGjLCw2Xi9\nlUNtxkFsr9pOdHA0o8JGdV8pOho++gjmzvX5Mdav73e7TZrGeVu2cG9aWr+Gn6Quyb08l1E/G0Xo\npENfxKdQ9IQSC8WQERV1GrW1yxlu+6l/ve9rjk89vveKZjP8+c/w8MNw+um+uFJ9xJCSq3Jz+UFE\nBD8f1YNIHQIFfywAIO23af16jkLRESUWiiEjODgdiyWMlpaBn110OByyWLRx4YXw5ZeweDHcfnuf\n/BiLCwqo0TQeHz++X0NydV/UUfZcGVmvZyHMw2doT3Hko8RCMaT4ehfLhtqMTny972tOSD3h8G7K\nzvb5MXJzD9uP8U5lJS+Xl/PO5MnYTH3/SrrL3eRelcvEVyZiT7L3+TkKRVcosVAMKVFRC6irWz7U\nZrRT1FBEi7eFzJg+LAqMioKPPz4sP8am5mZ+np/Pu9nZJNj6PnNJ6pLcK3JJui6J6FOj+/wchaI7\n+iUWQog/CCE2CSE2CCE+FUIkdrh2txAiXwiRK4RY0KE8RwixWQiRJ4R4tEO5TQixxH/PKiHEocdi\nVhyxREXNp7HxW3TdNdSmAPuHoPo8FHQYfoxqj4eFW7fy+Lhx5ISF9dFiH4V/KgQD0n6X1q/nKBTd\n0d+exYNSymlSyhnAx8C9AEKIScDFQBZwJvCU2P/texpYJKXMBDKFEG2bBSwCaqWU44FHgQf7aZvi\nCMBiiSA0dCoNDV8PtSlAH4eguqIXP4bXMLho+3YujY/n0oSEfjVV9986Sp8tJesN5adQDBz9Egsp\nZXOH01CgLdLaucASKaUmpSwA8oE5/p5HmJRyjb/eK8BC//F5wMv+43eALlZEKY5GoqJOo65uePgt\nVu5beXjO7Z7owY9x+65dhJpM/Ck9vV9NtPspXlZ+CsXA0m+fhRDiT0KIfcDlwO/8xaOBog7VSvxl\no4HiDuXF/rJO90gpdaBeCKEGX0cA0dELqKn5eMiHoupd9eyt38uMxBmBe+iBfowNG3i+tJTP6+p4\nfdIkzP2Y+aS7dHKvzCVpURLRp6mvimJg6XVvSiHEcqBjP1kAErhHSvmhlPI3wG+EEHcBNwOLA2Rb\nj9+ixYv3NzNv3jzmzZsXoGYVg01Y2BxCQyexdu00MjOfISpq/pDYsaZkDTlJOVjNAQ470ubHmD6d\nz3/5S37zm9+w8phjiLD0fWtYV7GLbRdsI2hMEGn3pgXOVsVRxYoVK1ixYkVAniUCtSBKCJECfCyl\nnCqE+BUgpZQP+K99is+fUQh8KaXM8pdfCpwkpfx5Wx0p5fdCCDNQJqWM76YtOdwWcim6Roj27ax7\npbr6ffLzbyYy8mQyMh7GZosdWOMO4P6V91PdWs1fT//rgDx/a3MzJ69dyzu//z0nXnYZXH99n55T\nv7Ke7ZdsZ/Qto0m9K3VYhUpRDG+EEEgp+/QH09/ZUOM6nC4EdviPPwAu9c9wSgfGAaullOVAgxBi\njt/hfTXwfod7rvEfXwT8tz+2KY48YmPPY/bsbVitUaxZM5ny8pcHdXX3urJ1zBw1c0CeXeZ2c/aW\nLfwtK4sTn3sOHnoIfv3rw9pQSUpJyZMlbLtwGxNenMCYX41RQqEYNPrVsxBCvANk4nNsFwI/k1KW\n+a/djW+Gkxe4VUq5zF8+E/gnEAR8IqW81V9uB14FZgA1wKV+53hX7aqexRHC4fQsOtLUtI6dO6/H\nYokgM/OZQdkMKf3v6Xx25Wd9W2PRAy26zkkbNnBebCy/bYskW10N554LaWnw0ktg79k5rbt08m/I\np2lNE9nvZROcERxQGxUjg/70LAI2DDWYKLE4cuirWIBv97ySkicoLPwTycm3kJp6FybTwMz4qWmt\nYexjY6m7qw6TCNxaVV1Kzt+6lRirlRcnTOjcE3A64aqrfLOk3n3XF5ywC1zFLrb9aBtBaUFMeHEC\nFkfffR2Kkc2QDUMpFAOJyWQhJeU2Zs1aT1PTWtaunU59/VcD0ta6snXMSJwRUKEA+MWuXbToOs9m\nZh48ZBQcDG+/7Zsl9YMfwN69B91f/1U96+esJ+6COCa9NUkJhWLIUH95imFPUFAq2dnvU139Hrm5\nVxAVtYCMjIewWgM3XXRd6TpmJgXWX/H34mI+r6vjmxkzuo/5ZDL5VnuPGeMTjPffh9mz2/0ThX8s\nJOuVLKJPV1NjFUOL6lkojgiEEMTFnc/s2dswmx1+B/hrAXOArytbx6xRswLyLID3q6t5YN8+Pp4y\nhUjrIUzFvflmePppOOss9Hc+YOe1Oyl7roycb3OUUCiGBcpnoRhQ+uOz6InGxjXk5V2PxRJDZubT\nhISM79fz0h5NY9lVywLi3F7b2MiZW7bwyZQpzA4PP6x7XR9+51s/MSWWiV+dgzm077vlKRQHonwW\nihFHePhscnLWEBNzJuvXz6Ww8D4Mw9OnZ9W01lDnqmNc9LjeK/dCocvFeVu38sKECYctFPX/q2f9\n9QZxt89kUvNdmO+967Cm1ioUA4kSC8URi88BfgezZq2joWEVa9ZkU1r6/GGHDVlftj4gzu1WXeeH\nW7ZwZ0oK58Ue+oJCaUiK/lrEtou3MfHliaQ+MB2x6ltYvRp+8hMlGIphgRILxRFPUNAYpkz5kMzM\nZ6mufo/vvkujoOCPeDzVh3T/juodTIqb1G87bsnPZ2poKLcmJx/yPZ5qD1vO3ULlvyrJ+T6H6AV+\n/0R0NHz6KeTnw69+1W/bFIr+osRCcVQghCAqaj5Tp37M9Olf4HIVsHr1ePLybqS1dVeP9+bV5PXb\nV/FaeTkrGxp4uqspst1Qv7KedTnrCJ0UyoyVMwhOO2ChXUgIfPSRL/3tb/2yT6HoL0osFEcdoaGT\nmTjxH8yevR2LJZL1649l69YLaGhY1WX9vNr+icWOlhZu372btydPJuwQggNKXVJ4XyHbLtpG5jOZ\nZDyYgcnazVexrYfxyCPw5pt9tlGh6C9qnYXiqMVuT2Ls2PtITb2b8vKXyM29ApstiZSUXxIbey6+\neJX961k4dZ2Lt2/nvvR0pjkcvdb3VHjIvTIXw20wc+1MgpKDem8kNRWWLoVTToHYWN/eGArFIKOm\nzioGlIGaOtsXDEOjuvpdiooeQtPqSE7+BZGxlxDz0Ciaf92MxXT4v51+unMnjbrOG1lZvQ4/1X1R\nR+7Vvv0nxvxuDCbLYXbsv/rKtwPf0qUwc2ACHiqOblRsKMWwZTiJRRtSShoavqao6GFq67/mozLB\nfQu3Y7N1GRG/W96sqOB3BQWsmzmT8B6GnwzNoPAPhZS9UMbEVyYSfWo/Ftm9+y7ceCOsXAkZGX1/\njmJEosRCMWwZjmLRkfe2PM6+or+SE95AXNzFpKT8gpCQCb3el9fayg82bGDZ1KnMCAvrtp67xM32\ny7cjrIKs17KwJwYgEOIzz/hChHzzDfRz/27FyEItylMo+siOhhaKLBcxZ85ObLZENmw4gS1bzqO+\nfmW3oURcus7F27bx+7S0HoWi5pMa1s5cS/SCaKZ9Ni0wQgHws5/BFVfAWWdBU1NgnqlQ9ILqWSgG\nlOHes7j2/WuZmzyX62ZeB4Cut1Je/jLFxY9gsUT7neHnY+rgz7ghL48qr5e3J03q0k9heA323rOX\nyjcryXoji8gTIgNvuJTw05/6ItV+/DHYbIFvQ3HUoXoWCkUfyavJY0Ls/mEnszmE0aN/zpw5O0hN\n/RXFxY+yenUmxcWPo2nNvF1ZyWe1tbxw4N4UfpwFTjaeuJGWbS3M3DBzYIQCfCr81FO+tRhqlbdi\nEFBioRjRdDdtVggzcXHnk5PzDVlZr1Ffv4Jvv0tn5Y47eWtiKhFdOLRrPqnx7T1xYRxTPpyCLXaA\nf+1bLLBkCRQWqlXeigFHiYVixOLSXDS4G0gI7dlJHBFxHNnZ/+a9qNc5LrgZb+6xVFe/36lO/cp6\ndvx4B9nvZZNyRwrCNEh7YwcHwwcfwMsvQ27u4LSpGJEosVCMWKpbq4kNiT2k8BzVHg8v1YVwyrQ3\nmTjxZXbv/n9s3Xo+LlcxLbktbLtwG1mvZRFxXMQgWH4A0dFw223wxz8OftuKEYMSC8WIpU0sDoXn\nyso4PzaWeJuNqKj5zJq1idDQaaxdM50Nj/yW9AfG7A8COBTcdBN8/jns2DF0NiiOapRYKEYsVS1V\nxIXE9VrPYxg8WVLSKZqs2RxEStxvsP35Gcxnf0PZlPNpatowkOb2TFgY3H676l0oBgwlFooRy6H2\nLN6pqmJCSEin2E+GZrD94u2Ej87mmHNXMWrUDWzefAa7dt2BpjUPpNndc9NNsHy56l0oBgQlFooR\nS1Vr7z0LKSV/Ky7mtg69Cikl+T/PBwmZT2diMplISvoJs2dvxeutYs2abKqrPxpo8w8mLEz5LhQD\nhhILxYjlUHoWqxobqfN6OTsmpr2s8L5CmtY1MentSZ1Ci9tscWRlvcKECS+we/ftbNt2EW536YDZ\n3yWqd6EYIJRYKEYsVS1VvYrF34uLuSU5GbN/xlT5K+WUvVDGlI+nYAnrOnhgdPSpzJq1mZCQiaxd\nO42SkqeQUg+4/V0SHg633gp/+tPgtKcYMSixUIxYqp3VxIV2Pwy1z+VieV0dP05MBKD281p237mb\nqUunYk/qOc6T2RxMevofmT79f1RUvMH69T+guXlTQO3vlptvhs8+U70LRUBRYqEYsfTWs3iypIRr\nEhMJt1ho3tRM7uW5TP7XZEKzQg+5jdDQScyY8RVJSf/Hpk2nsXv3/0PXWwJhfveEh/t8F6p3oQgg\nSiwUI5bq1upuHdwtus4/ysq4efRoXEUutpyzhfGPjyfyxMOP9SSEiVGj/o/Zs7fgdpewZk02NTVL\n+2t+z7T1LnbuHNh2FCOGgIiFEOIOIYQhhIjuUHa3ECJfCJErhFjQoTxHCLFZCJEnhHi0Q7lNCLHE\nf88qIURqIGxTKLqjqrX7nsUr5eWcEBlJqsfKlrO2MPrW0cRfcnibIx2IzZbApEmvk5n5LPn5N7Ft\n26W43eX9ema3KN+FIsD0WyyEEMnAaUBhh7Is4GIgCzgTeErsj6nwNLBISpkJZAohTveXLwJqpZTj\ngUeBB/trm0LRE3XOOqKCo7q89mZlJdcnJVF4XyFhs8NIuSMlYO1GRy9g9uytBAWlsGnTKQM3LHXL\nLfCf/0DzEK37UBxVBKJn8TfgzgPKzgOWSCk1KWUBkA/MEUIkAmFSyjX+eq8ACzvc87L/+B3glADY\nplB0iW7oaIaG3Xywo1pKyabmZmYFOSh/uZzUu1MPKX7U4WA2BzN27IOEheWQn39zQJ/dTng4ZGfD\nhiFcWa44auiXWAghzgWKpJRbDrg0GijqcF7iLxsNFHcoL/aXdbpH+uYZ1ncc1lIoAolLcxFkCepS\nBGqMWPkAABBASURBVApcLsLMZuTSBkKyQggZHzIgNgghGD/+aRoavqW8/NUBaYNZs2Dt2oF5tmJE\n0f0u836EEMuBjjGcBSCB3wC/xjcENRD0+FNu8eLF7cfz5s1j3rx5A2SG4mikTSy6YmNzM9McDspe\nKGPUdaMG1A6LxcHkyf9i06aTCQubTWjoxMA2MGuWb5GeYkSyYsUKVqxYEZBn9XlbVSFENvA50Irv\nxZ6MrwcxB7gWQEr5F3/dT4F78fk1vpRSZvnLLwVOklL+vK2OlP+/vbsPjqO+7zj+/konWZIlZBsI\nfpAtI7B4qhPbYAOFgEMGYkIJDNNQhmmhKbRMIQmFmSahJAMzSRuStAmlGZiUhwQIHddNZ6iTEAKt\nI3dIY+NiwIB5ECS2ZRnbGCHZYEnW6b79Y1f1Wpa897A63XGf18wOq592Vx/Ouvvqt/v77fp6M6sG\n3nb3Ma8o6rGq5aNUH6vavbebZQ8so/vW7sO+d+fvfkd11xCfuGI3Z28/m+q66gnPs2PH/XR338OS\nJeuprk6wJ/Pyy3DFFfDGG8kdU8rWpDxW1d1fdveZ7t7m7scTnFJa7O67gdXAH4UjnI4HTgSedfed\nQJ+ZLQsveF8DjDxFZjVwbbj+WWBNvtlE4hypZ/HiBx+wZPUBjrv6uKIUCoBZs65n6tSFvPnmzcke\n+OSTYccO6O1N9rhScZKcZ+GEp47cfTOwCtgMPAHcGOkK3AQ8CLwBdLr7k2H7g8AxZtYJ/BWg50TK\nhOlP949bLDb17aNp5V5mXT+raHnMjPb2H9DbG8z4TkwqBYsWwcaNyR1TKlLsNYtsuXvbqK+/CXxz\njO2eAxaO0T5IMNxWZMKN17PoS6eZ++sDTJ3TSONHG8fYc+KkUk2ceuoqNm26kKamM2hoOPzZ4HlZ\nuhQ2bIALLkjmeFKRNINbKtJAeoD6VP1h7Zvef58//EU1s/68eL2KqKamRcyf/3VeeeVKhocHkjmo\nRkRJAlQspCKN17N45be9tG8cLni2diFmz76BhoaTeOutW5I5oIqFJEDFQipS/9DY1yz6H3uXDy5p\nGvf248VgZpx00v309DzN7t3/WvgBFyyAnh54553CjyUVS8VCKtJYPQt3Z+6q9znmupmTlOqgVOoo\nTjttFZ2dn2f//jcLO1hVFZx+Ojz3XDLhpCKpWEhFGqtYvLvmPfbVOh8778iPWi2WpqYltLbewebN\nCVy/WLpUp6KkICoWUpHGKha/ffRt1v1BiqNqaiYp1eHmzLmJurpWuroKvK/m4sW6R5QURMVCKtJQ\nZoiaqkOLwgdr++j5+NhzLyaLmdHW9i26u/+JdHpv/gdqa4MtWxLLJZVHxUIq0tDwEDXVB4vFQNcA\nmX3D1J06MTcNLERDQzvTp1/Ijh335X+Q+fNh69bYzUTGo2IhFWl0z6J3bS/vLZ3C3LrS6lmMaG29\nna6u7+X/7Itjj4X9+/VsC8mbioVUpHQmTarq4PDYvrV9bDkjxdwphz/fohRMnXoazc3nsGPH/fkd\nwAzmzVPvQvKmYiEVafRpqN6OXjYtgpYSLRYw0rv4Tv4jo1pbVSwkbyoWUpGip6EGuwcZem+IF1vS\nJduzgGAobWPjYnbu/GF+B1CxkAKoWEhFivYsetf2Mu28aWwbOlCy1yxGtLbezrZtd5HJDOW+8/z5\nGhEleVOxkIoU7Vn0ru2l/twmBjMZZqQm7zYf2WhuPpv6+gXs2vXj3HdWz0IKoGIhFSl6gbu3o5f9\nv99Ay5QpYz6Tu9TMn/81tm37OzKZdG47qlhIAVQspCKNnIYafHuQoXeG2HliVUlfr4hqbj6P2tqZ\nvPPOqtx2VLGQAqhYSEUaOQ3V9999NH+8ma6hA2VTLMyM1tavsnXr3+KeyX7H2bNhzx4YSOg5GVJR\nVCykIg1lgp5Fb0cv05ZPY/vgYMlf3I6aPv0iqqsb2LPn8ex3qq6Glhbo6pq4YPKhpWIhFSmdSVNT\nVROMhDp/Gl2DgyU9x2K0oHfxNbZu/QYHH2+fBZ2KkjypWEhFSmfSpPpTDGwdoPFjjbw9OMjs2trJ\njpWTo4++lOHhD9i3L4dbj7e0QHf3xIWSDy0VC6lIGc+Q6ktRc2wNVm30ZzJMra6e7Fg5MTOampaw\nf//r2e80dWpwjyiRHKlYSEXKeIbqvdXUzAjmWgxkMtRVld/boa7uBAYG3sp+h/p66O+fuEDyoVV+\n7w6RBLg7VX1VpGYEcy0GMhmmlGGxqK8/gf7+HB67Wlen0VCSl/J7d4gkwPGgZzG9vHsW9fUn0t+v\nnoVMvPJ7d4gkIOOZw3oW5VksTsitWKhnIXkqv3eHSALcnaq9VWV/zaK2dhbDw++TTu/Lbgf1LCRP\n5ffuEEmAExSLcu9ZmBn19W3Z9y7Us5A8ld+7QyQBGc9gfVb2PQvIcUSUehaSp4LeHWZ2h5ltN7ON\n4bIi8r3bzKzTzF41s4si7UvMbJOZvWFmd0faa81sZbjPb8xsXiHZRI7E3bE+IzUjhbsHo6HK4I6z\nYwkucmc5Iko9C8lTEn9Kfdfdl4TLkwBmdgpwJXAKcDFwrx289/N9wHXu3g60m9mnwvbrgB53XwDc\nDXw7gWwiY3LCobPTU6TdqTIjVaY9i5wucqtnIXlK4t0x1p9jlwEr3T3t7luATmCZmc0Emtx9Q7jd\nI8DlkX0eDtd/AnwygWwiY4qehirnU1CQ4/BZ9SwkT0m8Qz5vZi+Y2QNm1hy2zQGit7bsDtvmANsj\n7dvDtkP2cfdhoNfMZiSQT+Qw0dNQ5V8scpiYp56F5Cn2HWJmT4fXGEaWl8L/XgrcC7S5+yJgJ/AP\nCWYrzxPIUhYchz4+FD2LKVPmceDATjKZwfiN1bOQPMU+cNjdL8zyWPcDPw3Xu4G5ke+1hG3jtUf3\n2WFm1cBR7t4z3g+78847/399+fLlLF++PMuYIlA1WAUZqKqvYqC/vItFVVWKurp5DAxsoaHhpCNv\nrJ5FReno6KCjoyORY1lO98IfvbPZTHffGa7fAix196vN7FTgMeBMgtNLTwML3N3NbB3wRWAD8HPg\nHnd/0sxuBH7P3W80s6uAy939qnF+rheSW4rHDErxn2rNa2to+d8W2v+4nb50mmf6+rjk6KMnO1be\nenp+SVPTGdTUxPw/7NsHHR1w6aVFySWlxcxw97zO2hRaLB4BFgEZYAtwg7vvCr93G8EIpyHgZnd/\nKmw/HfgRUAc84e43h+1TgEeBxcC7wFXhxfGxfq6KhYhIjiatWEwWFQsRkdwVUizK90StiIgUjYqF\niIjEUrEQEZFYKhYiIhJLxUJERGKpWIiISCwVCxERiaViISIisVQsREQkloqFiIjEUrEQEZFYKhYi\nIhJLxUJERGKpWIiISCwVCxERiaViISIisVQsREQkloqFiIjEUrEQEZFYKhYiIhJLxUJERGKpWIiI\nSCwVCxERiaViISIisVQsREQkloqFiIjEUrEQEZFYKhYiIhKr4GJhZl8ws1fN7CUzuyvSfpuZdYbf\nuyjSvsTMNpnZG2Z2d6S91sxWhvv8xszmFZpNRESSUVCxMLPlwKXAQndfCPx92H4KcCVwCnAxcK+Z\nWbjbfcB17t4OtJvZp8L264Aed18A3A18u5BspaCjo2OyI2RFOZNTDhlBOZNWLjkLUWjP4i+Bu9w9\nDeDue8L2y4CV7p529y1AJ7DMzGYCTe6+IdzuEeDyyD4Ph+s/AT5ZYLZJVy6/QMqZnHLICMqZtHLJ\nWYhCi0U7cJ6ZrTOzX5nZ6WH7HKArsl132DYH2B5p3x62HbKPuw8DvWY2o8B8IiKSgFTcBmb2NHBc\ntAlw4Kvh/tPd/SwzWwr8G9CWUDaL30RERIrC3fNegCeA8yNfdwJHA18BvhJpfxI4E5gJvBppvwq4\nL7pNuF4N7D7Cz3UtWrRo0ZL7ku/nfWzPIsbjwAXAWjNrB2rd/V0zWw08ZmbfJTi9dCLwrLu7mfWZ\n2TJgA3ANcE94rNXAtcB64LPAmvF+qLur1yEiUkSFFosfAg+Z2UvAIMGHP+6+2cxWAZuBIeBGD7sE\nwE3Aj4A64Al3fzJsfxB41Mw6gXcJeh0iIlIC7OBnuIiIyNjKYga3mU03s6fM7HUz+6WZNR9h2yoz\n2xieCiuqbHKa2RQzW29mz4cTGe8o0ZwtZrbGzF4Jc36xFHOG2z1oZrvMbFMRs60ws9fCyaVfHmeb\ne8JJpi+Y2aJiZRuV4Yg5zewkM/sfMxsws1snI2OYIy7n1Wb2Yrg8Y2YLSzTnZ8KMz5vZs2Z2Tinm\njGy31MyGzOyK2IMWcoG7WAvwLeBL4fqXCeZ2jLftLcCPgdWlmhNoiFzIXwcsK7WcBIMRFoXrjcDr\nwMmlljP83rnAImBTkXJVAW8CrUAN8MLo14ZgMurPw/UzgXXFfO1yyHkMcDrwdeDWYmfMIedZQHO4\nvqKEX8+GyPpCIgN6SilnZLv/An4GXBF33LLoWXDohL2HOTiR7xBm1gJ8GnigSLlGyyqnu+8PV6cQ\nXDcq9rnA2JzuvtPdXwjX3wde5eCcmGLJ9vV8BnivWKGAZUCnu2919yFgJUHWqMsIJp3i7uuBZjM7\njuKKzenue9z9OSBd5GxR2eRc5+594ZfrKP7vImSXc3/ky0YgU8R8I7L5/QT4AsEE6N3ZHLRcisVH\n3H0XBB9iwEfG2e57wF9T/A/fEVnlDE+VPQ/sBJ72gzPaiyXb1xMAM5tP8Jf7+glPdqicchbR6Emn\n0cml423TPcY2Ey2bnKUg15zXA7+Y0ERjyyqnmV1uZq8CPwX+rEjZomJzmtls4HJ3v48s57QVOhoq\nMTGT/0Y7rBiY2SXALnd/Ibxn1YQMry00J4C7Z4DFZnYU8LiZnerum0stZ3icRoK/Pm4OexiJSiqn\nVAYz+wTwOYJTjyXJ3R8neF+fC3wDuHCSI43lboJTuyNiPy9Lpli4+7gvaHjx8jh33xXeX2qsbtM5\nwGfM7NNAPdBkZo+4+zUlljN6rL1m9iuCc7CJFoskcppZiqBQPOru/5FkviRzToJuIHpX5JawbfQ2\nc2O2mWjZ5CwFWeU0s48C/wyscPdinnYckdPr6e7PmFmbmc1w954JT3dQNjnPAFaamRFct7rYzIbc\nfdyBQeVyGmo18Kfh+rXAYR9c7v437j7P3dsI5misSbpQZCE2p5kdMzKqx8zqCf7qeK1YAUOxOUMP\nAZvd/R+LEWoM2eaE4C+jYk3W3ACcaGatZlZL8Ps2+k22mnDekZmdBfSOnFIromxyRk3WZNfYnBY8\nsuDfgT9x97cmISNkl/OEyPoSgonKxSwUkEVOd28Ll+MJ/iC88UiFYmSnkl+AGcB/EozIeQqYFrbP\nAn42xvbnMzmjoWJzEoyQ2EgwQmETcHuJ5jwHGA5zPh9mXlFqOcOv/wXYQTAxdBvwuSJkWxHm6iS8\ntQ1wA/AXkW2+TzAq5UVgSbH/nbPJSXAKsAvoBXrC16+xBHPeTzBZd2P4+/hsib6eXwJeDnP+Gji7\nFHOO2vYhshgNpUl5IiISq1xOQ4mIyCRSsRARkVgqFiIiEkvFQkREYqlYiIhILBULERGJpWIhIiKx\nVCxERCTW/wFGT7QJMsVjZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9865e18890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print Rd_GS, zphi.shape, vd[:, 0], vd[:, 1]\n",
    "plt.figure()\n",
    "for i in range(vd.shape[1]):\n",
    "    plt.plot(vd[:, i], -zphi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dx_GS = gsw.earth.distance([GS[0]-.5,GS[0]+.5], [GS[1],GS[1]])[0][0]\n",
    "dy_GS = gsw.earth.distance([GS[0],GS[0]], [GS[1]-.5,GS[1]+.5])[0][0]\n",
    "dx = 1e3; dy = 1e3\n",
    "Nx_GS = 100\n",
    "Ny_GS = 100\n",
    "k = fft.fftshift( fft.fftfreq(Nx_GS, dx) )\n",
    "l = fft.fftshift( fft.fftfreq(Ny_GS, dy) )\n",
    "\n",
    "k_GS = k[np.absolute(k) < 5.*Rd_GS[1]**-1]\n",
    "l_GS = l[np.absolute(l) < 5.*Rd_GS[1]**-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.82748556522e-06 0.000193893247747 1.16549711304e-07\n",
      "(39,) (100,)\n"
     ]
    }
   ],
   "source": [
    "print (2*dx_GS)**-1, 5.*Rd_GS[1]**-1, (Nx_GS*dx_GS)**-1\n",
    "print k_GS.shape, k.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50,) (50,) 85800.2983284 111194.926645 (49,)\n",
      "<xarray.DataArray (Depth_c: 49)>\n",
      "array([  -10.00006115,   -20.00006114,   -30.00006113,   -40.00006112,\n",
      "         -50.0000611 ,   -60.00006109,   -70.00256358,   -80.01506451,\n",
      "         -90.06006328,  -100.20256308,  -110.5900682 ,  -121.51007976,\n",
      "        -133.44509256,  -147.10512803,  -163.43519352,  -183.5678012 ,\n",
      "        -208.72298023,  -240.09073926,  -278.70109317,  -325.30655719,\n",
      "        -380.30712264,  -443.70276088,  -515.09343642,  -593.72659767,\n",
      "        -678.57216844,  -768.44015906,  -862.11055267,  -958.45325725,\n",
      "       -1056.53586028, -1155.72595435, -1255.87608991, -1357.68378579,\n",
      "       -1463.12934261, -1575.64299213, -1699.66489761, -1839.65538686,\n",
      "       -1999.10931291, -2180.15155508, -2383.76440293, -2610.28273438,\n",
      "       -2859.78914881, -3132.29607118, -3427.80347989, -3746.3113517 ,\n",
      "       -4087.81966191, -4452.32838443, -4839.83749199, -5250.34695635,\n",
      "       -5683.85674858])\n",
      "Coordinates:\n",
      "    Longitude_t  float32 299.5\n",
      "  * Depth_c      (Depth_c) float32 15.0 25.0 35.0 45.0 55.0 65.0 75.005 ...\n",
      "    Latitude_t   float32 39.5\n",
      "    Time         datetime64[ns] 2005-07-02\n"
     ]
    }
   ],
   "source": [
    "print u_GS.shape, v_GS.shape, dx_GS, dy_GS, zN2_GS.shape\n",
    "print zN2_GS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "etax = np.zeros(2)\n",
    "etay = np.zeros(2)\n",
    "# kwargs = {'v0':np.ones(len(zN2_GS)+1), 'num_Lanczos':int(1e10), 'iteration':int(1e7), 'tol':1e-5}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exponential profiles\n",
    "\n",
    "#### w/out lateral viscosity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zpsi, w, psi = baroclinic.instability_analysis_from_N2_profile( -zN2_GS.values, \n",
    "                                                                   N2_fit, f0_meta.sel(Latitude_t=GS[1]).values,\n",
    "                                                                   beta_meta.sel(Latitude_t=GS[1]).values,\n",
    "                                                                   k_GS, l_GS, z_t.values, u_fit, v_fit, etax, etay,\n",
    "                                                                   Ah=0., num=2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(39, 39) 681 (50, 39, 39)\n"
     ]
    }
   ],
   "source": [
    "print w[0].shape, np.argmax(w.imag[0]), psi[:, 0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f98649e8f90>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4cpVDh5mZWcuQ4Fe/gldegS9/Oe9qWocDhzWBzHClCiZrF4eNgnHMZRzlh1dl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\nas3/AyTR5mkhapRBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9864bc8c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(13,5))\n",
    "\n",
    "ax1 = fig.add_subplot(121)\n",
    "cax = ax1.contourf(k_GS*Rd_GS[1], l_GS*Rd_GS[1], w.imag[0], 20)\n",
    "cbar = fig.colorbar(cax, orientation='vertical')\n",
    "ax1.set_xlabel(r'$k/K_d$', fontsize=14)\n",
    "ax1.set_ylabel(r'$l/K_d$', fontsize=14)\n",
    "ax1.set_title(r'$\\sigma$', fontsize=18)\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax2.plot(np.reshape(psi[:, 0], (len(zpsi), psi.shape[-1]**2))[:, np.argmax(w.imag[0])], -zpsi)\n",
    "ax2.set_ylabel(r'Depth [m]', fontsize=12)\n",
    "ax2.set_title(r'$\\psi$', fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### w/ lateral viscosity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zpsi, w, psi = baroclinic.instability_analysis_from_N2_profile( -zN2_GS.values, \n",
    "                                                                   N2_fit, f0_meta.sel(Latitude_t=GS[1]).values,\n",
    "                                                                   beta_meta.sel(Latitude_t=GS[1]).values,\n",
    "                                                                   k_GS, l_GS, z_t.values, u_fit, v_fit, etax, etay,\n",
    "                                                                   Ah=1e1, num=2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f98660a1a50>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DP/szeDS9Y76UrNbFJXdfAWxSdTsAhYwyJMZ0lK2Xykba9qq7V+UxLWww9XUj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f98668bff90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(13,5))\n",
    "\n",
    "ax1 = fig.add_subplot(121)\n",
    "cax = ax1.contourf(k_GS*Rd_GS[1], l_GS*Rd_GS[1], w.imag[0], 20)\n",
    "cbar = fig.colorbar(cax, orientation='vertical')\n",
    "ax1.set_xlabel(r'$k/K_d$', fontsize=14)\n",
    "ax1.set_ylabel(r'$l/K_d$', fontsize=14)\n",
    "ax1.set_title(r'$\\sigma$', fontsize=18)\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax2.plot(np.reshape(psi[:, 0], \n",
    "                    (len(zpsi), psi.shape[-1]**2))[:, np.argmax(w.imag[0])], -zpsi)\n",
    "ax2.set_ylabel(r'Depth [m]', fontsize=12)\n",
    "ax2.set_title(r'$\\psi$', fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Actual profile\n",
    "#### w/out lateral viscosity ($A_h=0$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zpsi, w, psi = baroclinic.instability_analysis_from_N2_profile( -zN2_GS.values, \n",
    "                                                                   N2_GS.values, f0_meta.sel(Latitude_t=GS[1]).values,\n",
    "                                                                   beta_meta.sel(Latitude_t=GS[1]).values,\n",
    "                                                                   k_GS, l_GS, z_t.values, u_GS.values, v_GS.values, etax, etay,\n",
    "                                                                   Ah=0., num=2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f98655d2690>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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h/nvhvHsuHuQwqUNwhGDQvJYPgiZvvZ8s0xRV3k+WMCXStxY9GrQ16DERPn+n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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f98658e1950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sig = w[0].copy()\n",
    "for i in range(10):\n",
    "    sig[:, i] = np.nan\n",
    "for j in range(10):\n",
    "    sig[j, :] = np.nan\n",
    "\n",
    "for i in range(-1,-10,-1):\n",
    "    sig[:, i] = np.nan\n",
    "for j in range(-1,-10,-1):\n",
    "    sig[j, :] = np.nan\n",
    "\n",
    "plt.imshow(sig.imag, origin='bottom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FVQ9XiU4N7Lfqlfa/EhERycVWWzVutPH220F7+QULgtuMGfCjHwVrv7baqnHw2nZbTTcs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f986584f910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,5))\n",
    "\n",
    "ax1 = fig.add_subplot(121)\n",
    "cax = ax1.contourf(k_GS*Rd_GS[1], l_GS*Rd_GS[1], w.imag[0], 20)\n",
    "cbar = fig.colorbar(cax, orientation='vertical')\n",
    "ax1.set_xlabel(r'$k/K_d$', fontsize=14)\n",
    "ax1.set_ylabel(r'$l/K_d$', fontsize=14)\n",
    "ax1.set_title(r'$\\sigma$', fontsize=18)\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax2.plot(np.reshape(psi[:, 0], (len(zpsi), \n",
    "                                psi.shape[-1]**2))[:, np.nanargmax(sig.imag)], -zpsi)\n",
    "ax2.set_ylabel(r'Depth [m]', fontsize=12)\n",
    "ax2.set_title(r'$\\psi$', fontsize=18)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### w/ lateral viscosity ($A_h=10$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "zpsi, w, psi = baroclinic.instability_analysis_from_N2_profile( -zN2_GS.values, \n",
    "                                                                   N2_GS.values, f0_meta.sel(Latitude_t=GS[1]).values,\n",
    "                                                                   beta_meta.sel(Latitude_t=GS[1]).values,\n",
    "                                                                   k_GS, l_GS, z_t.values, u_GS.values, v_GS.values, etax, etay,\n",
    "                                                                   Ah=1e1, num=2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f9865393150>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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52tbTtU9Z/Filbr22ECpoK9Aa2i69uG2TxQe4IPizzbiK0Ps8WXwds/BTTood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\nGFuICd8wthATvmFsISZ8w9hC/n/TBCYhh05lfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f986568aa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sig = w[0].copy()\n",
    "for i in range(15):\n",
    "    sig[:, i] = np.nan\n",
    "for j in range(15):\n",
    "    sig[j, :] = np.nan\n",
    "\n",
    "for i in range(-1,-15,-1):\n",
    "    sig[:, i] = np.nan\n",
    "for j in range(-1,-15,-1):\n",
    "    sig[j, :] = np.nan\n",
    "\n",
    "plt.imshow(sig.imag, origin='bottom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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d4O4T3H2iu99ctn2Ju+/r7nu7+yfLtr/s7seH22e6+2MpfzsiIpt5/nn41reC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\nMzMzMzNL5P8DrYoaqc1mhWUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f986aaee590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,5))\n",
    "\n",
    "ax1 = fig.add_subplot(121)\n",
    "cax = ax1.contourf(k_GS*Rd_GS[1], l_GS*Rd_GS[1], w.imag[0], 20)\n",
    "cbar = fig.colorbar(cax, orientation='vertical')\n",
    "ax1.set_xlabel(r'$k/K_d$', fontsize=14)\n",
    "ax1.set_ylabel(r'$l/K_d$', fontsize=14)\n",
    "ax1.set_title(r'$\\sigma$', fontsize=18)\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax2.plot(np.reshape(psi[:, 0], (len(zpsi), psi.shape[-1]**2))[:, np.nanargmax(sig.imag)], -zpsi)\n",
    "ax2.set_ylabel(r'Depth [m]', fontsize=12)\n",
    "ax2.set_title(r'$\\hat{\\psi}(z)$', fontsize=18)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ACC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ACC = np.array([141.5, -51.5])\n",
    "u_ACC = u_coinT.sel(Longitude_t=ACC[0], Latitude_t=ACC[1])\n",
    "v_ACC = v_coinT.sel(Longitude_t=ACC[0], Latitude_t=ACC[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "absS_ACC = absS_meta.sel(Longitude_t=ACC[0], Latitude_t=ACC[1])\n",
    "consT_ACC = consT_meta.sel(Longitude_t=ACC[0], Latitude_t=ACC[1])\n",
    "rho_ACC = rho_anom.sel(Longitude_t=ACC[0], Latitude_t=ACC[1])\n",
    "potrho_ACC = potrho_meta.sel(Longitude_t=ACC[0], Latitude_t=ACC[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N2_ACC = N2_meta.sel(Longitude_t=ACC[0], Latitude_t=ACC[1])\n",
    "zN2_ACC = zN2_meta.sel(Longitude_t=ACC[0], Latitude_t=ACC[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BV199xeWXX15kxxEpqdLT07nqqqv4z3/+Q/v27Vm4cKGGvihllJty0nVT6eWc\nY8mSJXz88cdMmTKF3bt351inQYMGtG/f/rii4RlnnOFBtCLhLZTykwqEIuKJHTt2sGTJEpYsWcLi\nxYtZsWIFR48ePW6dSpUq0aFDBzp37kz37t3p1q0b1apV8yhiya9QSnKhoqhz0/33389rr73G888/\nzyOPPFJkxxEpiTIyMhg8eDAff/wx1apVY+XKldSvX9/rsKSYKTflpOsmAd9n5IYNG7KGDFqxYgWr\nVq3K8Xc5+IqGsbGxtG/fnssuu4xmzZp5ELFIeAml/KQCoYiEhLS0NNasWcPixYuzCodbt249bh0z\no3Xr1vTo0YMePXpwwQUXcPbZZ3sUseQllJJcqCjq3DR+/Hhuv/12hgwZwrvvvltkxxEpaTIyMhgy\nZAgffPABVapUYdasWXTq1MnrsMQDyk056bpJ8pKenp6jaLh69eocRcP+/fvz17/+lT59+mgMQ5FT\nFEr5SQVCEQlZe/bsyephuGDBApYuXZpj1uQmTZpkFQt79OhBvXr19AeKx0IpyYWKos5Nc+fOpU+f\nPnTp0oVFixYV2XFESpK0tDRuvvlmPvzwQypVqsSMGTOOm2hLShflppx03SQFkZ6enjU54YIFC5g8\neTIpKSkAtGnThgceeICBAwdSrlw5jyMVKVlCKT+pQCgiJUZKSgo//PADCxYs4LvvvmPRokUcOXLk\nuHXq1q2bVSzs06cPjRs39ija0iuUklyoKOrclJSUxA8//ECzZs0455xziuw4IiVFUlIS11xzDbNn\nz6ZixYpMmzaNnj17eh2WeEi5KSddN8np2LdvH+PHj+eNN97IGsOwVq1a3H333dx1111UqVLF4whF\nSoZQyk8qEIpIiXXs2DFWrVrFd999x3fffcf3339PQkLCces0atSI/v37c8kll9CzZ08qVKjgUbSl\nRygluVCh3CRSfHbu3Mkll1xCXFwcZ511FlOnTiU2NtbrsMRjyk05KTdJYUhNTeWf//wn//d//8e6\ndesA6NKlC999950mGxTJh1DKTyoQikjYyMzMJD4+nu+++4758+cze/bs4wqGFSpUoHfv3lxyySX0\n79+fBg0aeBht+AqlJBcqlJtEise6deu45JJL2LZtG02aNOGbb77RZ70Ayk25UW6SwuScY9asWQwd\nOpQdO3bw9NNPM2LECK/DEgl5oZSfVCAUkbCVnp7ODz/8wDfffMO0adNYtWrVce0xMTFZvQt79OhB\nVFSUR5Hl8oVpAAAgAElEQVSGl1BKcqFCuUmk6H322WfcdNNNJCcn07VrV/79739To0YNr8OSEKHc\nlJNykxSFb7/9lgsvvJDIyEiWLFlC+/btvQ5JJKSFUn5SgVBESo1du3Yxffp0vvnmG2bOnEliYmJW\nW8WKFbnooou47rrruOyyy6hYsaKHkZZsoZTkQoVyk0jRycjI4IknnmDUqFEAXH/99bzzzjsaUkKO\no9yUk3KTFJV7772X119/nWbNmrFixQp9HoucQCjlJxUIRaRUOnbsGEuWLGHatGlMmzaNNWvWZLVV\nrlyZq6++mkGDBtGnTx+Nn1JAoZTkQkVx5ibnnGbyllLj4MGDDBo0iGnTplGmTBleeukl7r//fv0O\nSA7KTTnpukmKSkpKCnXq1CEhIYHp06dz8cUXex2SSMgKpfykAqGICLB9+3Y+//xzPvzwQ5YtW5a1\n/Oyzz2bgwIEMGjSI2NhYXXTmQygluVBRHLlp//79XHjhhSQmJrJ169YiPZZIKIiPj+eqq65i06ZN\nVK9enU8++YQLL7zQ67AkRCk35aTrJikqy5Yto2PHjlStWpVt27ZpRmOREwil/KQCoYhINps2beKj\njz7io48+YvPmzVnLmzRpwvXXX8+gQYNo1KiRhxGGtlBKcqGiOHJTZmYm0dHRJCcns3fvXmrWrFmk\nxxPx0qRJk7j99ts5cuQIbdq04YsvvqB+/fpehyUhTLkpJ103SVG57rrrmDx5Mg899BAvvvii1+GI\nhLRQyk8qEIqI5ME5x7Jly/jnP//J5MmT2b17d1Zb165dueeee7jmmmt0C3I2oZTkQkVx5aYuXbqw\nZMkS5syZQ+/evYv8eCLF7ciRI9x1111MnDgRgBtuuIHx48dTqVIljyOTUKfclJOum6Qo/PrrrzRo\n0AAzY+vWrdStW9frkERCWijlpzJeByAiEqrMjI4dO/Laa6+xfft2ZsyYwY033kjlypVZtGgRAwcO\npEGDBrz44oskJCR4Ha4IrVu3BiAuLs7jSEQK34YNG+jYsSMTJ06kfPny/OMf/2DSpEkqDoqIhJDn\nnnuOjIwM/vjHP6o4KFLCqEAoIpIPkZGR/P73v+f999/nt99+Y9y4ccTExLBt2zYeeeQRzjnnHO64\n4w42btzodahSigUKhMGT7oiEgw8++IDY2Fji4+Np2rQpS5cuZejQoRoXVkQkhEybNo3x48cTGRnJ\nI4884nU4IlJAKhCKiBRQpUqVuO2221i/fj3Tpk3j97//PUeOHGHcuHE0bdqUAQMGMGvWLHTbjhS3\nNm3aAGiSEgkbycnJ3Hzzzdx4440cOXKEG264gWXLltGqVSuvQxMRkSC7d+9myJAhADzzzDNZX1qK\nSMmhMQhFRApBfHw8o0eP5oMPPuDo0aMAtGjRgkcffZSBAweWqnEKQ2kcjVBRXLkpNTWVPXv2cM45\n56hnlZR4q1at4rrrrmPjxo2UL1+eN954Q70G5ZQpN+Wk6yYpLJmZmQwYMIDp06fTp08fZs2aRZky\n6oskkh+hlJ9UIBQRKUT79u3jrbfeYuzYsezatQuAxo0bM2LECK6//vpSUSgMpSQXKpSbRPIvMzOT\n0aNH8+ijj5KWlkaLFi2YPHkyLVu29Do0KcGUm3JSbpLCMnr0aO677z6qV6/OmjVrqFOnjtchiZQY\noZSfVCAUESkCaWlpfPjhh4waNSrrds+GDRsyfPhwbrjhBsqWLetxhEUnlJJcqFBuEsmf3bt38+c/\n/5np06cDcMcdd/Dyyy9ToUIFjyOTkk65KSflJikM69evp23btqSlpfHFF19w5ZVXeh2SSIkSSvlJ\n/X5FRIpAuXLlGDp0KD/++CPvvfcejRo1YsuWLQwdOpSYmBjeeecd0tLSvA6zVDOzeWaWYmZJZnbI\nzDZka+9rZhvM7LCZfWtm52Zrf8HM9pnZXjN7vnijFwk/06dPp3Xr1kyfPp3q1avz5ZdfMnbsWBUH\npdRRfpKSwjnHfffdR1paGjfffLOKgyIlnAqEIiJFqGzZstx0001s2LCBDz74gCZNmvDzzz9zyy23\n0KRJE95++22OHTvmdZillQPucM5FO+eqOOeaBRrMrAbwGTAcqA6sAD4Jah8GXA60AloDl5nZrcUZ\nvEi4SE1N5f7776d///7s2bOH3r17s2bNGq644gqvQxPxivKTlAhTp05l1qxZVKtWjeefVy1apKRT\ngVBEpBhERkZyww03sH79ej766COaNm3Kf//7X4YNG0azZs2YPHkymZmZXodZGuXVnf9qYJ1z7nPn\nXBowEmhjZk387TcCrzjndjnndgEvA38u6mDzKyMjg/Xr12smbQl58fHxdOrUiddee43IyEiee+45\nZs2apfGrRMI0P0n4SEtL44EHHgDgiSeeoGbNmh5HJCKnSwVCEZFiFBERwfXXX8+6deuYPHkyTZo0\nYcuWLVx33XXExsYyc+ZMFXWK13NmtsfMFphZz6DlLYC4wBvn3BFgs395jnb/6xaEiKZNm9KiRYus\n8S9FQo1zjjFjxhAbG0tcXBwNGzZk4cKFPProo0RERHgdnkgoCMv8JOFj7NixbNq0iZiYGO68806v\nwxGRQqACoYiIByIiIvjTn/5EfHw8b7/9NrVr12bVqlVcfPHF9O3bl6VLl3odYmnwMNAAqANMAL42\ns/r+tspAYrb1k4AqebQn+ZeFhJiYGABWrFjhcSQiOe3atYv+/ftzzz33cPToUYYOHcqqVavo2LGj\n16GJhIqwzU8SHvbu3cuTTz4JwCuvvBLWk++JlCaRXgcgIlKaRUZGcssttzBo0CDeeOMNnnvuOebO\nnUunTp245ppreOaZZ2jatKnXYZY4ZjYX6IlvHKfsFjrnejjnlgUtm2Rm1wGXAGOBw0B0tu2qAof8\nr7O3V/Uvy9PIkSOzXvfq1YtevXqd9DxOVbt27Zg6dSorVqzgj3/8Y5EdR6SgvvrqK/7yl7+wb98+\nqlevzoQJE7j66qu9DkvC0Lx585g3b57XYeQQavmpOHOThI+nn36axMREfv/733PJJZd4HY5IiRKq\n+QnAwu1WNjNz4XZOIlJ6JCQk8OKLLzJ69GhSUlIoU6YMd999N6NGjaJSpUpeh5cvZoZzLq+xk0KW\nmU0Dpjnn3jCzW4CbnHPd/W2VgL1AG+fcJjNbCLzrnPuHv/1m4GbnXNc89l2suemrr77iyiuvpG/f\nvsyePbvYjiuSl+TkZO6//34mTJgAwIUXXsh7772nsQal2JTU3ARFl5903SSn4rfffuO8884jNTWV\nuLg4Wrdu7XVIIiVaKOWnQrnF2MzuNLNlZnbUzN7Npb2vmW0ws8Nm9q2ZnZut/QUz22dme83s+Wxt\n9cxsjpklm9l6M+tbGDGLiISiM844g+eee47Nmzdz2223YWaMHj2aNm3asGDBAq/DCxtmVtXMfm9m\nUWYWYWaDgAuA6f5VvgBamNlVZhYFPAGsds5t8rdPAh4ws9pmVgd4AJhY3OeRl/bt2wOwcuVKjWkp\nnlu2bBlt27ZlwoQJlCtXjldffZUZM2aoOCiSi3DPT1LyvfLKK6SmpnLllVeqOCgSZgprDMIdwNPA\nP7I3mFkN4DNgOFAdWAF8EtQ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IyMjwOBo5ma1bt9KlSxc++eQTqlSpwpdffsmTTz6pCzyR\n8FUqc5MUjWPHjjFs2DAAHnroIVq00H8FEcmb/roUESkFYmJiABUIs6kMZO9SmQRUyaM9yb8sP9uG\nrDPOOIPvvvuObdu2aVKLEBcYb3Dt2rXExMSwdOlSrrjiCq/DEpGiVSpzkxSNV199lbVr19KgQQNG\njBjhdTgiEuIivQ5ARESKXjgWCM1sLtATcLk0L3TO9TjJLg4D0dmWVQUO5dFe1b8sP9vmMHLkyKzX\nvXr1olevXicJr+hccMEFnh1b8mfcuHHcfffdZGRkcNlll/HBBx/olmKRApg3bx7z5s0r9uMqN0mo\n+OWXX7L+fd98800qVqzobUAiAniXn/JDBUIRkVKgadOmQHgVCJ1zvU9zF/HATYE3ZlYJaAisC2pv\nAyz3vz/fvyzQ1sDMKgXdytUG+DCvgwVfhInkJSMjg4ceeohXX30VgOHDh/PUU0/plmKRAspe7Hry\nySeL5bjKTRIKnHPceeedpKSkMHDgQC6++GKvQxIRP6/yU37or00RkVIgUCBct27dSdYML2YWYWbl\ngQgg0syizCxwX+0XQAszu8rMooAngNXOucB0z5OAB8ystpnVAR4AJgL411kNPOHf59VAS3wzT4qc\nksOHD3P11Vfz6quvUrZsWd577z2eeeYZFQdFwoxykxS1zz77jGnTplG1atWsL5xERE7GnMut93vJ\nZWYu3M5JROR0paamUqVKFdLT00lMTKRKlaIbjsjMcM5ZkR2gAMzsCXwXV8GJ4Unn3FP+9j7AWOBc\n4Afgz865X4O2fx64xb/9BOfc34LazgXeBzoB/wXucM7NzSMO5SY5oZ07d3LppZeyatUqzjjjDL74\n4gt69uzpdVgiYUO5Kdc4lJvCUFJSEs2aNWPnzp2MGzeO2267zeuQROQEQio/hVtSUKITEcldbGws\nK1asYO7cuUU6xlAoJblQEaq5KS0tjYiICE1W4rG4uDguvfRStm/fTqNGjZg6dSpNmjTxOiyRsKLc\nlFOo5iY5PXfccQfjxo2jc+fOLFy4UL3QRUJcKOUnfVqIiJQSHTp0AGD58uUnWVNKgyuvvJJKlSoR\nFxfndSil2tSpU+nevTvbt2+ne/fuLF68WMVBERE5JfPnz2fcuHGULVuWCRMmqDgoIgWiTwwRkVIi\nUCBctmyZx5FIKIiKiiI9PZ21a9d6HUqpNWbMGC6//HIOHz7MoEGDmD17NjVr1vQ6LBERKYGOHDnC\nX/7yF8A3wVXLli09jkhEShoVCEVESonY2FhAPQjFJ3DhUNomrgkFGRkZ3Hvvvdxzzz1kZmYycuRI\nPvjgA6KiorwOTURESqiRI0eyefNmWrZsyd/+9reTbyAikk2k1wGIiEjxaN68ORUqVGDr1q3s37+f\nGjVqeB2SeKhFixaACoTFLS0tjcGDBzNlyhTKlSvHu+++y6BBg7wOS0RESrBly5bxyiuvUKZMGf7x\nj39Qrlw5r0MSkRLotHsQmlk5M3vHzH4xs0QzW2lm/bKt09fMNpjZYTP71j+7VnD7C2a2z8z2+mfl\nCm6rZ2ZzzCzZzNabWd/TjVlEpDSKjIykXbt2APzwww8eRyNea968OQAbNmzwOJLSIzk5mcsuu4wp\nU6YQHR3NzJkzVRwUEZHTkpaWxs0330xmZib3338/HTt29DokESmhCuMW40jgV+AC51xV4O/AlEAR\n0MxqAJ8Bw4HqwArgk8DGZjYMuBxoBbQGLjOzW4P2/7F/m+rACOBT/z5FRKSAunfvDsCCBQs8jkS8\n1rBhQyIjI0lJSSE1NdXrcMLegQMHuOiii5g5cyZnnXUW8+bNo2fPnl6HJSIiJdzzzz/P2rVradSo\nEU899ZTX4YhICXbaBULn3BHn3FPOuW3+91OBn4H2/lWuBtY55z53zqUBI4E2ZhaYou9G4BXn3C7n\n3C7gZeDPAP512gIjnXOpzrnPgTXANacbt4hIaXTBBRcAKhAKlC1blj179rB7926NfVfEdu3aRc+e\nPVm8eDH16tXj+++/p23btl6HJSIiJVx8fDzPPPMMABMmTKBixYoeRyQiJVmhT1JiZmcDTYDAoEYt\ngLhAu3PuCLDZvzxHu/91oK05sNU5l5xHu4iIFEC3bt0wM5YuXUpKSorX4YjHzjjjDK9DCHtbtmyh\nW7durFu3jmbNmvH999/TuHFjr8MSEZESLj09nT//+c8cO3aMYcOG0atXL69DEpESrlAnKTGzSOBD\nYKJzbpN/cWVgT7ZVk4AqQe2J2doq59EWaK99ojhGjhyZ9bpXr176sBQR8atWrRqtW7cmLi6OpUuX\nFsotjvPmzWPevHmnH5xImFmzZg0XX3wxv/32Gx06dGDatGnUrFnT67BERCQMPP/88yxfvpxzzz2X\nF1980etwRCQMnLRAaGZzgZ6Ay6V5oXOuh389w1ccTAXuDlrnMBCdbbuqwKE82qv6l+Vn21wFFwhF\nRNVnOaAAACAASURBVOR4F1xwAXFxcSxYsKBQCoTZv4h58sknT3ufIiXdokWLGDBgAAcPHqRPnz58\n+eWXVKlS5eQbioiInERcXFzWeIPvvvsu0dHZL5lFRArupLcYO+d6O+fKOOcicnn0CFr1H0BN4Grn\nXEbQ8njg/MAbM6sENOR/tyDHA22C1j/fvyzQ1sC/TUCboHYRESmgHj18H93fffedx5GIhKcZM2Zw\n4YUXcvDgQa666iqmTp2q4qCIiBSKtLQ0brrpJo4dO8Ydd9xB3759vQ5JRMJEoYxBaGbjgabA5f6J\nSIJ9AbQws6vMLAp4AlgddAvyJOABM6ttZnWAB4CJAP51VgNPmFmUmV0NtMQ3K7KIiJyCwEQlixcv\nJj093eNoxGupqals2LCBjIyMk68sJzV9+nQuv/xyUlJSGDp0KFOmTKF8+fJehyUiImHimWeeIS4u\njgYNGvDCCy94HY6IhJHTLhCa2bnArfh6/u02s0NmlmRm1wE45/bhm3X4WeAAEAsMDGzvnHsL+Br4\nf/buPE7P+V78/+udfRkJiRBJyEJiifXQquOUOOnR1E7aU5zaT+V7iupBlaOtoIsW51TRWnqqUYqW\n8KuqOrUMihLaRElIiIhEFgnZJslMZD6/P+a+0zGZmUwyy3Uvr+fjcT/c9/W+rut+X3ONfOZ+35/l\nb9QtQPLblNJt9d7iROATwIfAd4HxKaWlrc1bksrVwIEDGTlyJKtWrWLq1KlZp6OM7bbbbuyxxx7M\nmTMn61SK3h//+EeOO+44ampqOO+88/jZz35Gly5tOt2zJKmMvfTSS3zve98jIrj99tupqKjY9EGS\n1EKtLhCmlObmhiD3SiltlXv0SSndXW+fJ1JKu6eUeqeU/jmlNLfBOS5JKfVPKW2bUrq0kfMfljv/\n7imlJ1ubsySVu4MPPhiomydN5W3nnXcGYObMmRlnUtyeeOIJjjnmGKqrq/mP//gPrr/+euqmZ5Yk\nqfWqq6s57bTTWL9+Peeff/6GKWMkqa20yRBjSVJx+cd//EfAAqFg1KhRgAXC1njqqac46qijWLt2\nLV/+8pe58cYbLQ5KktrU5ZdfzvTp0xk1ahTf+973sk5HUgmyQChJZSjfg/DZZ5/NOBNlbeTIkYAF\nwi31pz/9iSOPPJI1a9ZwxhlncPPNN9Opk39eSZLazp///GeuueYaOnXqxKRJk+jZs2fWKUkqQf4F\nK0llaLfddmPrrbdm3rx5vPvuu1mnowzlexDOmjVrE3uqoeeff57Pfe5zVFVVccopp3DbbbdZHJQk\ntak1a9Zw+umnU1tby0UXXcSnPvWprFOSVKL8K1aSylCnTp02DDO2F2F5GzVqFAMHDqRfv35Zp1JU\nXnzxRcaNG8eqVas4+eSTuf322+ncuXPWaUmSSsw3v/lN3njjDfbYYw+uuOKKrNORVMIsEEpSmXIe\nQkHdEOMFCxZwzz33ZJ1K0Xj55Zc5/PDDWbFiBf/6r//KpEmTLA5Kktrcn/70J/7nf/6Hzp07M2nS\nJHr06JF1SpJKmAVCSSpTzkMobb6ZM2fy2c9+luXLlzN+/HjuvPNOunTpknVakqQSU1VVxRlnnEFK\niUsvvZQDDjgg65QklbhIKWWdQ5uKiFRq1yRJ7aGqqoq+ffsCsGzZMioqKtrkvBFBSsklXOuxbSoN\nixcv5qCDDmL27Nl87nOf48EHH6Rbt25ZpyWpBWybNmbbVNi++tWvcsMNN7D33nszZcoU2xupRBVS\n+2QPQkkqU71792bfffdl/fr1vPzyy1mnIxW0qqoqjjrqKGbPns3+++/Pr3/9az+sSZLaRWVlJTfc\ncANdunRh0qRJtjeSOoQFQkkqY//wD/8AwNSpUzPORCpcH330ESeeeCJTpkxh+PDhPPzww23W41aS\npPpWrVrFGWecAcC3vvUt9t1334wzklQuLBBKUhnL/9FpgbC8VVdXM23aNBesaURKiXPPPZff/e53\n9OvXj0ceeYTtt98+67QkSSXqkksuYc6cOey3335ceumlWacjqYw4q7YklbH99tsPgL/+9a8ZZ6Is\nvf766+y7777svvvuTJ8+Pet0CsrVV1/NLbfcQvfu3fntb3/LrrvumnVKkqQS9dxzz/GTn/yELl26\n8Itf/IKuXbtmnZKkMmIPQkkqY3vttRcRwfTp06mpqck6HWVkxIgRAMyePZva2tqMsykcv/zlL/mv\n//ovIoK77rprw8rfkiS1tZqaGs4++2xSSlx88cXsvffeWackqcxYIJSkMlZRUcHIkSNZt26dPcfK\n2FZbbcWAAQOorq5mwYIFWadTEB5//HHOPPNMAH70ox8xfvz4jDOSJJWyH/7wh7z22muMHDmSb33r\nW1mnI6kMWSCUpDLnMGPB33sRvvXWWxlnkr0ZM2Zwwgkn8NFHH3HhhRfy1a9+NeuUJEkl7I033uCq\nq64C4JZbbqFHjx4ZZySpHFkglKQy50IlAhg+fDgA77zzTsaZZGvlypWccMIJrFixgs9//vP88Ic/\nzDolSVIJq62tZcKECdTU1HDGGWdw2GGHZZ2SpDLlIiWSVOYsEApg//33Z/HixWy99dZZp5KZlBJn\nnnkmr7/+OqNHj+b222+nUye/S5UktZ/bb7+dp556igEDBnDttddmnY6kMhYppaxzaFMRkUrtmiSp\nPS1cuJAddtiBPn368OGHH7a6IBIRpJSijdIrCbZNxeG6667joosuYquttuKll15i1KhRWackqY3Y\nNm3Mtil7ixYtYrfddmPZsmX86le/4qSTTso6JUkdrJDaJ78Wl6QyN3DgQLbffntWrFhRcsNLI+Kc\niJgSEWsj4ucNYkMjojYiVkTEytx/L2uwzw8iYklEvB8RVzdy/BMRURUR0yNibEdck9pHZWUl3/jG\nNwCYNGmSxUFJ7ca2SXlf+9rXWLZsGePGjePEE0/MOh1JZc4hxpIkdt99dxYtWsQbb7yxYS66EjEf\nuAr4LNCzkXgC+jbWhSIiJgDHAHvlNj0WEbNTSrfmXt8NPAt8DjgSuC8idkkpLW3ja1A7mz9/Pl/8\n4hdZv349l1xyCccff3zWKUkqbbZN4umnn+aee+6hV69e/PSnPyWiIDoQSSpj9iCUJLHrrrsCdavo\nlZKU0oMppd8CHzSxS9B0W3gqcF1KaUFKaQFwLXA6QESMAvYDJqaUqlNKk4FXgPFtmb/aX01NDV/4\nwhdYvHgxY8eO3bCKpCS1F9sm1dbWctFFFwFw8cUXM2zYsGwTkiQsEEqS+HuBcObMmRln0uESMCci\n5kbEzyOif73YaGBavdfTctsA9gBmp5SqmoirSFx44YU8//zzDBkyhLvvvpsuXRxcISlztk0l7t57\n72XKlCnssMMOGwqFkpQ1/wqWJG2Yb63UehBuwhLgE8BUoD/wE+AuYFwuXgEsr7f/ity2xmL5+KCm\n3mzixIkbno8ZM4YxY8ZsceLtZdq0abz++usceuihDBw4MOt02t2dd97JjTfeSLdu3bj//vsZMGBA\n1ilJaiOVlZVUVlZmncaWsG0qcWvXruXSSy8F4Dvf+Q69e/fOOCNJHamQ2ydXMZYk8eabbzJy5EiG\nDBnCu+++26pzddRKXBHxJHAodT0tGno2pXRIvX2vAganlM5s5nzbAwuArVJKVRGxDPhMSumlXHx/\n4ImUUt+IOA74Tkppz3rH3wDUppTOb+TcRdE2HXXUUTz88MM88MADHHfccVmn065mzZrFvvvuy+rV\nq7n55puZMGFC1ilJake2TcXbNpWaa665hosvvpi99tqLv/71r3Tu3DnrlCRlyFWMJUkFZdiwYXTt\n2pV58+ZRVVW16QMKQErpsJRSp5RS50Yeh2z6DI2flr+3ja8B+9SL7Zvblo+NiIj6X/vvUy9elIYM\nGQLAvHnzMs6kfa1fv57TTjuN1atXc9JJJ3H22WdnnZKkEmHbpOYsWbKE7373uwBce+21FgclFRQL\nhJIkunTpws477wzU9awqFRHROSJ6AJ2BLhHRPSI652KfjIhRUac/cD3wZEppZe7wO4ALImJQRAwG\nLgBuB0gpzaJu+NfluXOeAOwJ3N+xV9i28gXC+fPnZ5xJ+7rmmmt4/vnnGTRoEDfddJMrR0rqULZN\n5euqq65i+fLlfPazn+Xwww/POh1J+hgLhJIkoGRXMv4msBr4BvBvueeX5WIjgD9QNz/TK8Ba4OT8\ngSmlW4CHgL9RN8n7b1NKt9U794nUzRP1IfBdYHxKaWl7Xkx7Gzx4MFDaPQhfeeUVvv3tbwPwv//7\nv2yzzTYZZySpDNk2laFZs2bxk5/8hE6dOnHNNddknY4kbcRFSiRJwN8XKimllYxTSlcAVzQRuwe4\nZxPHXwJc0kRsLnBYa3MsJKXeg7CmpoZTTz2VdevWMWHCBMaNG7fpgySpjdk2ladLLrmEjz76iLPO\nOou99tor63QkaSMWCCVJQMn2INRm2HnnnTnqqKPYf//9s06lXVx55ZVMmzaNESNGcO2112adjiSp\nTEydOpXJkyfTq1cvrrzyyqzTkaRGWSCUJAEWCAUjRozgoYceyjqNdvHCCy/w/e9/n4jgF7/4BRUV\nFVmnJEkqEz/84Q8BmDBhAoMGDco4G0lqXJTa0vYRkUrtmiSpIyxYsIBBgwbRv39/lixZssXniQhS\nSq76UI9tU7ZWr17Nfvvtx8yZM7nooouc+0kqQ7ZNG7Nt6hhvv/02I0eOJCKYPXs2O+64Y9YpSSog\nhdQ+uUiJJAmA7bffnu7du7N06VJWrly56QOkInHppZcyc+ZMRo8ezVVXXZV1OpKkMvLf//3frF+/\nnpNPPtnioKSCZg9CSdIGu+22G2+88QbTpk1j77333qJzFNK3YIXCtik7lZWVHHbYYXTp0oUXXniB\nf/iHf8g6JUkZsG3amG1T+1uyZAk77bQTa9as4ZVXXnFxEkkbKaT2yR6EkqQNhg8fDsCcOXOyTURq\nA+vWreMrX/kKAJdddpnFQUlSh7rppptYs2YNRxxxhMVBSQXPRUokSRvkC4Rvv/12xpkoKzNnzuS5\n555j5MiRHHzwwVmn0yo33ngjM2bMYOedd+bSSy/NOh1JUhmpqqrihhtuAODiiy/OOBtJ2jR7EEqS\nNhg2bBhggbCcPfLII5xxxhn86le/yjqVVlm0aBETJ04E4Prrr6d79+7ZJiRJKiu33347S5cu5cAD\nD+SQQw7JOh1J2iQLhJKkDRxirB122AGoW9W6mF1yySWsWLGCI488kiOPPDLrdCRJZeSjjz7iuuuu\nA+p6D0YUxPRiktQsC4SSpA0cYqxSKBD++c9/5he/+AXdunXjRz/6UdbpSJLKzG9+8xvmzJnDyJEj\nOfbYY7NOR5JapE0KhBHxy4hYEBHLIuL1iDirQXxsRMyIiFUR8XhE7NQg/oOIWBIR70fE1Q1iQyPi\niYioiojpETG2LXKWJG2sfoHQlQ3LU7EXCNevX8+5554LwIUXXsguu+yScUaSpHJz4403AnDRRRfR\nuXPnjLORpJaJtvgAGBF7ALNTSmsjYhTwFHBESumvEdEfeAs4E/gd8B3g0ymlg3LHTgC+Bvxz7nSP\nAdenlG7NxZ8DngW+CRwJ/C+wS0ppaRO5JD/UStKWSSnRp08fVq1axdKlS+nXr99mnyMiSCk5lqae\nYmqbqqqqqKiooFu3bqxdu7bohkXddtttnH322QwePJjXX3+dioqKrFOSVABsmzZWTG1TMZk1axaj\nRo2id+/eLFy40HZIUrMKqX1qkx6EKaXpKaW1uZcBJGDn3OsTgFdTSpNTSjXARGCfXCER4FTgupTS\ngpTSAuBa4HSA3D77ARNTStUppcnAK8D4tshbkvRxEeEw4zLXu3dvTjvtNL7yla+wbt26rNPZLB9+\n+CH/9V//BcC1117rhzJJUof75S9/CcD48eNthyQVlTabgzAiboqIKmAG8B7w+1xoNDAtv19KaTXw\nZm77RvHc83ws3zOxqom4JKmNWSDUL37xC/7nf/6Hbt26ZZ3KZvn2t7/NkiVLOPTQQ/niF7+YdTqS\npDJTW1u7oUB42mmnZZyNJG2eNisQppTOASqAfwImA9W5UAWwvMHuK4CtmoivyG1rybGSpDY2dOhQ\nAObOnZtxJlLLvfLKK/zkJz+hU6dO/PjHPy66odGSpOL3zDPPMGfOHHbccUfGjBmTdTqStFm6bGqH\niHgSOJS6YcMNPZtSOiT/IjeJxXMRcQrwH8CNwCqgT4Pj+gIrc88bxvvmtjUWa3hsoyZOnLjh+Zgx\nY/zHWZI2w4477gjAvHnzWrR/ZWUllZWV7ZiRtGkXXnghtbW1nHvuuey9995ZpyNJKkN33HEHAKec\ncgqdOrVZXxxJ6hCbLBCmlA7bwvPm5yB8DdjQvzoieudir9aL7wO8lHu9b25bPjYiInrXG2a8D3Bn\nc29ev0AoSdo8+QLhu+++26L9G34Rc8UVV7RHWlKTnnnmGR577DH69Onj758kKROrV6/mN7/5DQCn\nnnpqxtlI0uZr9dcaETEgIr4YEb0jolNEfBY4kbrViAEeAEZHxPER0R24HJiaUpqVi98BXBARgyJi\nMHABcDtAbp+pwOUR0T0iTgD2BO5vbd6SpMYNGTIEaHmBUMra5ZdfDsB//ud/btHK25IktdaDDz7I\nypUrOfDAA9l1112zTkeSNtsmexC2QKJuOPFPqSs4vgOcn1J6GCCltCQixgM3Udfz7wXqCojk4rdE\nxHDgb7lz3ZZSuq3e+U8EJgEf5s49PqW0tA3yliQ1YnOHGKv0vP322/z+979n0KBBHH/88Vmn06zK\nykqefPJJ+vbty9e+9rWs05Eklan88GJ7D0oqVlE3bWDpiIhUatckSR1p3bp1dO/enYigurqaLl02\n77ukiCCl5AoR9RRb2/TQQw9xzDHHcMQRR/Dwww9nnU6TUkqMGTOGp59+miuuuIJvf/vbWackqUDZ\nNm2s2NqmQvbee++x44470rlzZxYuXGhvdkktVkjtkzOnSpI+pmvXrgwcOJDa2loWLFiQdTrKwHbb\nbQfA4sWLM86keU8++SRPP/00W2+9Neeff37W6UiSytRdd91FbW0tRx99tMVBSUXLAqEkaSObu1CJ\nSsv2228PwKJFizLOpGkppQ1zD1500UX07ds344wkSeXqrrvuAhxeLKm4WSCUJG3EhUrK24ABA4C6\nHoSFOvzs8ccf509/+hP9+vXjvPPOyzodSVKZmjt3LtOmTaOiooJx48ZlnY4kbTELhJKkjbhQSXnr\n3bs3vXv3prq6mpUrV2adzkYa9h7s06dPxhlJkspVfq7eww8/nO7du2ecjSRtubZYxViSVGIcYqxz\nzz2Xzp07F2QPwj/+8Y8899xz9O/fn3PPPTfrdCRJZSxfIDzyyCMzzkSSWscCoSRpI/khxvYgLF9X\nX3111ik0qn7vwa9//etstdVWGWckSSpXq1ev5vHHHwfgiCOOyDgbSWodhxhLkjZiD0IVqkcffZQ/\n//nPDBgwgHPOOSfrdCRJZezJJ59k7dq1HHDAAQwcODDrdCSpVSwQSpI2ku9BOH/+/IwzkT7uqquu\nAuDiiy+moqIi42wkSeXM4cWSSkkU4txCrRERqdSuSZI62tq1a+nZsyddu3alurqaiGjxsRFBSqnl\nB5QB26a2MWXKFD75yU+y9dZbM2/ePHr37p11SpKKhG3TxmybWielxLBhw5g7dy4vvvgin/jEJ7JO\nSVIRKqT2yR6EkqSN9OjRg4qKCtatW8eKFSuyTkcC4IYbbgDg3//93y0OSpIy9eqrrzJ37ly23357\n9t9//6zTkaRWs0AoSWrUgAEDAHj//fczzmTLRES3iPhZRMyJiOUR8ZeIGNdgn7ERMSMiVkXE4xGx\nU4P4DyJiSUS8HxFXN4gNjYgnIqIqIqZHxNiOuK6O8uabb/LDH/6Qu+++O+tUAFi0aBH33nsvEcFX\nvvKVrNORpC1i21Q68sOLjzjiCDp18mO1pOLnv2SSpEYVe4EQ6ALMBT6dUuoLfAv4df6DVkT0B+4H\nLgP6AS8D9+YPjogJwDHAXsDewNERcXa989+dO6Yf8E3gvtw5S8LMmTP5xje+waRJk7JOBYBbb72V\nmpoajjnmGIYPH551OpK0pWybSoTzD0oqNRYIJUmN2m677QBYvHhxxplsmZTS6pTSlSmld3OvHwbe\nBvLjgE4AXk0pTU4p1QATgX0iYlQufipwXUppQUppAXAtcDpAbp/9gIkppeqU0mTgFWB8x1xd+9t2\n220BWLJkScaZQE1NDT/96U8B+OpXv5pxNpK05WybSsPSpUt57rnn6Nq1K//yL/+SdTqS1CYsEEqS\nGlUCPQg/JiK2B0YBr+Y2jQam5eMppdXAm7ntG8Vzz/OxPYDZKaWqJuJFr5AKhPfffz8LFixg9OjR\nHHbYYVmnI0ltxrapOD366KPU1tby6U9/mj59+mSdjiS1CQuEkqRGlVKBMCK6AHcCt6eUZuU2VwDL\nG+y6AtiqifiK3LaWHFv08ve/EAqE+cVJzjvvvM1aUVuSCpltU/H6wx/+ADi8WFJp6ZJ1ApKkwlTo\nBcKIeBI4FEiNhJ9NKR2S2y+o+wBWDZxXb59VQMOv/fsCK5uI981ta8mxG5k4ceKG52PGjGHMmDFN\n7VoQKioq6NatG1VVVaxZs4aePXtmkseUKVN4/vnn2XrrrfnSl76USQ6Sik9lZSWVlZUd/r62TeXh\n2WefBeCf//mfM85EUrHJqn1qCQuEkqRG5QuEhToHYUqppWNN/xfYFjgipbS+3vbXgNPyLyKiN7Az\nfx/m9RqwD/BS7vW+uW352IiI6F1vKNc+1H3Ya1T9D2HFICL4xje+Qffu3amtrc0sj3zvwbPOOove\nvXtnloek4tKw2HXFFVd0yPvaNpW+hQsXMnv2bCoqKthzzz2zTkdSkcmqfWoJhxhLkhqVX6SkUHsQ\ntkRE3AzsBhyTm+y9vgeA0RFxfER0By4HptYb5nUHcEFEDIqIwcAFwO0AuX2mApdHRPeIOAHYk7qV\nJ0vGlVdeyWWXXZZZYW7RokXce++9RATnnHNOJjlIUluzbSpuzz//PAAHHnggXbrY30ZS6fBfNElS\nowp9iPGmRMROwNnAWmBRbu66BExIKd2dUloSEeOBm6jrXfECcGL++JTSLRExHPhb7rjbUkq31XuL\nE4FJwIfAO8D4lNLS9r+y8nHrrbdSU1PDsccey/Dhw7NOR5Jazbap+D333HMA/OM//mPGmUhS24qU\nGpseo3hFRCq1a5KkLLzzzjsMGzaMIUOG8O6777b4uIggpeRKEvXYNm2+mpoahg0bxoIFC3jssccY\nO3Zs1ilJKmK2TRuzbdoyBx98MM899xyPPPII48aNyzodSUWukNonhxhLkhpVfw5CP0Coo02ePJkF\nCxawxx57OAm8JKkgVFdX89JLddM/fupTn8o4G0lqWxYIJUmN6tWrF71796ampoaVK5tcAFFqFzff\nfDMA5513HrkheJIkZeovf/kLNTU1jB49mq233jrrdCSpTVkglCQ1qW/fvgAWCMvUtGnTuPzyy/nN\nb37Toe87Z84cnnrqKXr06MHJJ5/coe8tSVJTnH9QUimzQChJalKvXr0AWL16dcaZKAt/+9vfuPLK\nK3nggQc69H3vvPNOAI4//nj69OnToe8tSVJTLBBKKmUWCCVJTerZsydggbBc9e/fH4APPvigw94z\npcQdd9wBwKmnntph7ytJUnNSShYIJZU0C4SSpCblexCuWbMm40yUhX79+gGwdOnSDnvPF198kVmz\nZjFw4EA+85nPdNj7SpLUnDlz5rBw4UL69+/PyJEjs05HktqcBUJJUpMcYlzesuhBmO89+G//9m90\n6dKlw95XkqTm1O896OJZkkqRBUJJUpMsEJa3ju5BWF1dzT333AM4vFiSVFgcXiyp1PnVvCSpSRYI\ny1vfvn257LLL2HbbbUkptXuPid///vd88MEH7LPPPuy9997t+l6SJG0OC4SSSp0FQklSk/KLlDgH\nYXnq3Lkz3/nOdzrs/VycRJJUiD766CNeffVVAA444ICMs5Gk9uEQY0lSk+xBqI6yZMkSHn74YTp1\n6sRJJ52UdTqSJG0wf/58PvroIwYNGrThbyNJKjUWCCVJTbJAqI5y7733sm7dOg4//HB22GGHrNOR\nJGmDt99+G4Bhw4Zlm4gktSMLhJKkJlkgVEdxeLEkqVDlC4TDhw/POBNJaj8WCCVJTbJAqI7w+uuv\n8+KLL7LVVltx7LHHZp2OJEkfM2fOHMACoaTSZoFQktQkFynRY489xte//nX+7//+r93e45e//CUA\nX/jCF5zbSZJUcBxiLKkcWCCUJDXJHoR67rnnuPbaa3nmmWfa5fy1tbXceeedgMOLJUmFySHGksqB\nBUJJUpMsEKp///4ALF26tF3O//TTTzN37lyGDh3Kpz/96XZ5D0mSWsMCoaRy0KYFwogYGRFrIuKO\nBtvHRsSMiFgVEY9HxE4N4j+IiCUR8X5EXN0gNjQinoiIqoiYHhFj2zJnSVLT8kOMLRCWr379+gHw\nwQcftMv5f/3rXwNw0kkn0amT31tKkgpLdXU17733Hp07d2bHHXfMOh1Jajdt/Zf4jcCL9TdERH/g\nfuAyoB/wMnBvvfgE4BhgL2Bv4OiIOLveKe7OHdMP+CZwX+6ckqR21rVrVwDWrVuXcSbKSr5A+OGH\nH7b5uWtra3nggQeAuvkHJUkqNHPnziWlxJAhQ+jSpUvW6UhSu2mzAmFEnAh8CDzeIHQC8GpKaXJK\nqQaYCOwTEaNy8VOB61JKC1JKC4BrgdNz5xwF7AdMTClVp5QmA68A49sqb0lS0ywQaptttgHapwfh\nc889x8KFCxk2bBj77bdfm59fkqTWcnixpHLRJl+BREQf4ArgMODLDcKjgWn5Fyml1RHxZm77zIbx\n3PPRued7ALNTSlVNxCVJ7cgCoYYPH873vve9dlm58f777wdg/PjxRESbn1+SpNaaM2cOYIFQ8tHd\n8QAAIABJREFUUulrqz7SVwK3pZTea+QP/ApgcYNtK4Ct6sWXN4hVNBHLxwe1NmFJ0qZZINSAAQO4\n9NJL2/y8KaWPFQglSSpE+R6E7fFFmSQVkk0WCCPiSeBQIDUSfhY4D/gMsG8Tp1gF9GmwrS+wsol4\n39y2lhzbqIkTJ254PmbMGMaMGdPc7pKkJrSkQFhZWUllZWUHZaRSMWXKFN59910GDx7MgQcemHU6\nkiQ1yiHGksrFJguEKaXDmotHxPnAUGBu1HUfrAA6R8QeKaUDgNeA0+rt3xvYGXg1t+k1YB/gpdzr\nfXPb8rEREdG73jDjfYA7m8upfoFQkrTlunXrBjRfIGz4RcwVV1zR3mmpBOR7D55wwgmuXixJKlgW\nCCWVi7b4i/wW6gp++1JXvLsZ+B1weC7+ADA6Io6PiO7A5cDUlNKsXPwO4IKIGBQRg4ELgNsBcvtM\nBS6PiO4RcQKwJ3WrIkuS2plDjNUeHF4sSSoWzkEoqVy0eg7ClNJaYG3+dUSsAtamlD7IxZdExHjg\nJup6/r0AnFjv+FsiYjjwN+qGMd+WUrqt3lucCEyiboXkd4DxKaWlrc1bkrRpFgjVHl555RXeeust\ntttuO/7pn/4p63QkSWpUdXU1ixcvplOnTuywww5ZpyNJ7aqtFinZIKW00diylNITwO7NHHMJcEkT\nsbnUrY4sSepgFggFMGnSJF588UXOPvts9tlnn1afL9978Pjjj6dz586tPp8kSe2hW7du9O7dm6qq\nKpYtW0a/fv2yTkmS2o2T/kiSmpQvENbU1GScibL0yCOP8JOf/ITp06e3yfnuu+8+wOHFkqTCFhHs\nsssuALz11lsZZyNJ7csCoSSpSfYgFMA222wDwIcfftjqc82YMYMZM2awzTbbfGxxG0mSCtHOO+8M\nwJtvvplxJpLUviwQSpKaZIFQwIYhVR988EGrz5UfXnzsscdu+P2SJKlQ2YNQUrmwQChJalIxFwgj\noltE/Cwi5kTE8oj4S0SMqxcfGhG1EbEiIlbm/ntZg3P8ICKWRMT7EXF1g9jQiHgiIqoiYnpEjO2o\na+to+R6EbVkg/PznP9/qc0lSsbFtKj72IJRULtp8kRJJUuko5gIhdW3cXODTKaV3I+JI4NcRsWdu\nASyABPRNKaWGB0fEBOAYYK/cpsciYnZK6dbc67uBZ4HPAUcC90XELimlpe14TZloqx6Eb731FlOn\nTqVPnz585jOfaYvUJKnY2DYVGXsQSioX9iCUJDWpmAuEKaXVKaUrU0rv5l4/DLwN7F9vt6DptvBU\n4LqU0oKU0gLgWuB0gIgYBewHTEwpVaeUJgOvACW56sZBBx3E9ddfz2mnndaq8+R7Dx511FF07969\nLVKTpKJi21R87EEoqVzYg1CS1KT6BcKUEhGRcUZbLiK2B0YBr9XbnIA5EZGAx4Cv1+tlMRqYVm/f\nabltAHsAs1NKVU3ES8ruu+/O7rvv3urz5AuErl4sSXVsmwrfkCFD6NatGwsXLqSqqorevXtnnZIk\ntQt7EEqSmtSpUye6dKn7LqkYexHmRUQX4E7g9pTSzNzmJcAngKHU9dzYCrir3mEVwPJ6r1fktjUW\ny8e3atvMS8e8efN48cUX6dWrF+PGjdv0AZJU4mybikPnzp0ZPnw44DBjSaXNHoSSpGb17NmTlStX\nsmbNGrp165Z1OhtExJPAodT1tGjo2ZTSIbn9groPYNXAefkdcj0s/pJ7+X5EnAssiIjeudgqoE+9\nc/bNbaORWD6+sql8J06cuOH5mDFjGDNmTPMXWGIefPBBAMaNG0evXr0yzkZSqausrKSysrLD39e2\nqTTtsssuvPHGG7z11lvsvffeWacjqYhl1T61hAVCSVKz6hcI+/btm3U6G6SUDmvhrv8LbAsckVJa\nv6nT8vfe9a8B+wAv5V7vy9+HgL0GjKj3gY3cvnc2deL6H8LK0QMPPADAcccdl3EmkspBw2LXFVdc\n0SHva9tUmpyHUFJbyap9agmHGEuSmtWzZ08A1qxZk3Emmy8ibgZ2A45JKdU0iH0yIkZFnf7A9cCT\nKaV8T4s7gAsiYlBEDAYuAG4HSCnNAqYCl0dE94g4AdgTuL9jrqy4LF26lKeeeoouXbpw1FFHZZ2O\nJGXKtqn4uJKxpHJggVCS1KwePXoAxVcgjIidgLOp612xKCJWRsSKiDgpt8sI4A/Uzc/0CrAWODl/\nfErpFuAh4G/UTfL+25TSbfXe4kTq5on6EPguML7eJPIl5/rrr+fUU09l5syZm965gYcffpj169cz\nZswYttlmm3bITpKKg21TcbIHoaRy4BBjSVKzirUHYUppLs18EZZSuge4ZxPnuAS4pJnzt3QoWdF7\n5JFHePTRR/niF7/IqFGjNuvY/PDi448/vj1Sk6SiYdtUnOxBKKkc2INQktSsYi0Qqm1tv/32ACxa\ntGizjlu9ejWPPvooAMcee2yb5yVJUnsbNmwYnTp1Yu7cudTU1Gz6AEkqQhYIJUnNskAo2PIC4aOP\nPsqaNWs48MADGTx4cHukJklSu+rWrRs77bQTtbW1zJkzJ+t0JKldWCCUJDXLAqFgywuErl4sSSoF\nzkMoqdRZIJQkNcsCoQAGDhwIwMKFC1t8zLp163jooYcA5x+UJBW3fDs4e/bsjDORpPbhIiWSpGbl\nC4Rr167NOBNl6aCDDuLWW29l9OjRLT7mqaeeYtmyZey+++7suuuu7ZidJEnt55lnnuHee+8FYM89\n98w4G0lqHxYIJUnNsgehAEaMGMGIESM265gHH3wQsPegJKl4zZ8/n89//vN89NFHXHTRRYwZMybr\nlCSpXTjEWJLULAuE2hK1tbUWCCVJRa26uprx48ezePFixo4dy/e///2sU5KkdmOBUJLULAuE2hIv\nvfQS8+fPZ8iQIey///5ZpyNJ0mb76le/ygsvvMBOO+3EPffcQ5cuDsCTVLosEEqSmmWBUFui/urF\nEZFxNpIkbZ6f/exn3HrrrXTv3p3Jkyez7bbbZp2SJLUrC4SSpGZZINSWyBcIHV4sSSo2L7zwAuec\ncw4At9xyiz3hJZUFC4SSpGb16NEDsEAouOmmmxg/fjzPP/98s/vNmDGDN954g379+nHIIYd0UHaS\nJLXeokWLGD9+PDU1NZxzzjmcdtppWackSR3CAqEkqVn2IFTeiy++yOTJk5kxY0az++V7Dx599NHO\n1yRJKhrr1q3jX//1X5k/fz4HH3ww//3f/511SpLUYSwQSpKaZYFQedtvvz1Q17uiOfnVi4877rh2\nz0mSpLby9a9/naeffpoddtiB++67j27dumWdkiR1GAuEkqRmWSBUXn6C9iVLljS5z+LFi5kyZQo9\ne/bk8MMP76jUJElqlTvvvJPrr7+erl27cv/99zNw4MCsU5KkDmWBUJLULAuEyluxYgUAffr0aXKf\nP//5zwAceOCB9OrVq0PykiSpNaZOncrZZ58NwI9//GMOOuigjDOSpI5ngVCS1CwLhMrL9xwcMGBA\nk/vkC4Sf+tSnOiQnSZJaY+nSpRx//PGsWbOGM888kwkTJmSdkiRlwpnDJUnNskCovHPOOYfDDjuM\n/fffv8l9XnjhBcACoSSp8K1fv56TTz6ZOXPm8IlPfIKbbrqJiMg6LUnKhAVCSVKzLBAqb6+99mKv\nvfZqMr5+/XpefPFFoG6IsSRJheyb3/wm//d//8eAAQO4//776dGjR9YpSVJmHGIsSWqWBUK11IwZ\nM1i1ahVDhw51cndJUkG7//77ufrqq+ncuTO//vWv2XHHHbNOSZIyZYFQktSsfIFw7dq1GWeiQuf8\ng5KkYjB9+nROP/10AK655hrGjBmTaT6SVAgsEEqSmmUPQrVU/RWMJUkqRMuXL+e4445j1apVnHzy\nyXzta1/LOiVJKggWCCVJzbJAqJZygRJJUiGrra3llFNOYdasWey9997cdtttLkoiSTkWCCVJzcpP\n2L127VpSShlno6y89dZbHH744Xz9619vNL5ixQpee+01unbtyn777dfB2UmStGlXXXUVDz30ENts\nsw0PPPAAvXr1yjolSSoYrmIsSWpWp06d6N69O9XV1axdu3ZDj0KVl3nz5vHHP/6R1atXNxqfMmUK\nKSX23XdfV4GUCsSwYcN45513sk6jwwwdOpQ5c+ZknYYK1FNPPcXEiROJCH71q18xYsSIrFOSpILS\nJj0II6IyItZExIqIWBkRMxrEx0bEjIhYFRGPR8RODeI/iIglEfF+RFzdIDY0Ip6IiKqImB4RY9si\nZ0lSyznMWO+//z4AAwYMaDTu8GKp8LzzzjuklMrmUU7FUG2+hx56CIDzzz+fcePGZZyNJBWethpi\nnICvpJT6pJS2Sintng9ERH/gfuAyoB/wMnBvvfgE4BhgL2Bv4OiIOLveue/OHdMP+CZwX+6ckqQO\nYoFQmyoQuoKxJKmQzZs3D4ADDjgg40wkqTC15RyETc3uegLwakppckqpBpgI7BMRo3LxU4HrUkoL\nUkoLgGuB0wFy++wHTEwpVaeUJgOvAOPbMG9J0iZYINSSJUuAxguEKaUNPQhdwViSVIjeffddAIYM\nGZJxJpJUmNqyQPj9iFgcEc9ExKH1to8GpuVfpJRWA2/mtm8Uzz3Px/YAZqeUqpqIS5I6QH5OOQuE\n5au5HoRz5sxh8eLFbLvtts7pJEkqSPkehBYIJalxbbVIycXAdKAGOAl4KCL2SSm9DVQAixvsvwLY\nKve8AljeIFbRRCwfH9RGeUuSWsAehDrvvPM4/PDD2X333TeK5YcXH3jggUQ0NaBAkqRsrF+/nvnz\n5wMwePDgjLORpMK0yQJhRDwJHErdPIMNPZtSOiSlNKXetjsi4iTgCOAmYBXQp8FxfYGVuecN431z\n2xqLNTy2URMnTtzwfMyYMYwZM6a53SVJm9BcgbCyspLKysoOzkgdbeTIkYwcObLRmAuUSJIK2aJF\ni1i/fj0DBgzYMCpCkvRxmywQppQO24LzJv4+J+FrwGn5QET0BnYGXq0X3wd4Kfd639y2fGxERPSu\nN8x4H+DO5t68foFQktR6zRUIG34Rc8UVV3RUWioQLlAiSSpk+fkHd9xxx4wzkaTC1eo5CCOib0Qc\nHhHdI6JzRPwb8GngD7ldHgBGR8TxEdEduByYmlKalYvfAVwQEYMiYjBwAXA7QG6fqcDlufOfAOxJ\n3arIkqQOMn78eC688EJ22mmnrFPZLBHxy4hYEBHLIuL1iDirQXxsRMyIiFUR8XhE7NQg/oOIWBIR\n70fE1Q1iQyPiiYioiojpETG2I66p0FRXV/PXv/6ViOATn/hE1ulIUsGzbep4zj8oSZvWFnMQdgW+\nA+wKrAdeB45NKb0JkFJaEhHjqRtufCfwAnBi/uCU0i0RMRz4G3U9D29LKd1W7/wnApOAD4F3gPEp\npaVtkLckqYUmTJiQdQpb6vvAl1NKayNiFPBURPwlpfTXiOhP3RdOZwK/o64tuxc4CCAiJgDHAHvl\nzvVYRMxOKd2ae3038CzwOeBI4L6I2KXc2qipU6dSU1PDHnvsQd++fbNOR5KKgW1TB8sXCO1BKElN\na3WBMKW0BPjkJvZ5Ath4VvO/xy8BLmkiNhfYkmHOkqQyl1KaXu9lUPdF1M7AX4ETgFdTSpMBImIi\nsCQiRqWUZgKnAtellBbk4tcCXwZuzX2g2w/4l5RSNTA5Is4HxgO3UkYcXiwVt/ZaWCilxqYvF9g2\nZSE/xNgehJLUtFYPMZYkqZBFxE0RUQXMAN4Dfp8LjQam5fdLKa0G3sxt3yiee56P7QHMrjc/bsN4\nSVm5ciU///nP+c1vfrNRbMqUunXKDjzwwI5OS1KJqq2t5aabbuKss87i5ZdfBmDhwoUccsghGWfW\ndmybOpY9CCVp09piiLEkSQUrpXRORJxL3fCsMUB1LlQBLG6w+wpgq3rx5Q1iFU3E8vFBTeVRfwGt\nhgu7FLqtttqKM888s9HYG2+8AcDo0WX/+VMqSoXY0+/BBx/kpJNO4vnnn+ftt99m//3357HHHmPw\n4MEtPkdlZSWVlZXtl2Qr2TZ1LHsQSioUhdw+RSH+UdAaEZFK7ZokqZhEBCml9hmz9vH3eRI4lLqh\nWQ09m1LaqKtJRPwUeC2ldGNE/AjoklI6t178b8C3U0oPRMQy4DMppZdysf2BJ1JKfSPiOOA7KaU9\n6x17A1CbUjq/kfctybYppcQ222zD8uXLWbRoEdttt13WKUmqJ/fvcdZpbLZVq1aRUmKXXXZhzpw5\n9OzZk3//93/noIMO4qyzzmryuOau17apfNqmxgwdOpS5c+fy1ltvMWLEiKzTkaQNOqp9agmHGEuS\nilJK6bCUUqeUUudGHk2NQ+tC3TxPAK8B++YDEdE7F3u1Xnyfesfum9uWj43IHZO3T714WXj//fdZ\nvnw5ffr0YcCAAVmnI6lEVFRU8Pvf/55DDjmEnj17AnU9LsaOHcuyZcsyzq55tk2FZ/369cyfPx+A\nQYOa7EwpSWXPAqEkqSRFxICI+GJE9I6IThHxWeBE4LHcLg8AoyPi+IjoDlwOTE0pzcrF7wAuiIhB\nETEYuAC4HSC3z1Tg8ojoHhEnAHtSt/Jk2Zg1q+5HNWrUqHZb6EBSeXr33XfZZZddAHj99ddZt24d\nO+64I3fffXfGmbWObVPHW7RoEevXr2fAgAH06NEj63QkqWA5B6EkqVQl4D+An1L3hdg7wPkppYcB\nUkpLImI8cBNwJ/ACdR/SyMVviYjhwN9y57otpXRbvfOfCEwCPsyde3xKaWm7X1UByRcIR44cmXEm\nkkrN+PHjueSSS7jvvvtIKXHQQQfx4x//mFNPPTXr1FrLtqmD5ecfdIESSWqeBUJJUklKKS2hbuL3\n5vZ5Ati9mfglwCVNxOYCh7UixaJngVBSexk+fDj33nvvhtdf+MIXMsym7dg2dbz8CsYuUCJJzXOI\nsSRJ2iIWCCVJhc4ehJLUMhYIJUnSFrFAKEkqdPYglKSWsUAoSZI2W0rJAqEkqeDlC4T2IJSk5lkg\nlCRJm23BggVUVVXRr18/+vXrl3U6kiQ1Kj/E2B6EktQ8C4SSJGmz5XsPjho1KuNMJElqmj0IJall\nLBBKkqTN5vBiSVIxqK6uBqB79+4ZZyJJhc0CoSRJ2mwWCCVJxWDgwIFA3dQYkqSmWSCUJEmbzQKh\nJKkYDBo0CLBAKEmbYoFQkiRtNguEkqRisMMOOwAWCCVpUywQSpKkzVJbW8ubb74JWCCUJBU2C4SS\n1DIWCCVJ0maZN28ea9euZbvttqNPnz5ZpyNJUpMsEEpSy1gglCRJmyU/vHjUqFEZZyJJUvMsEEpS\ny1gglCRJm8X5ByVJxcICoSS1TJesE5AkScXFAqFUWiKi0e0ppTbZX8pSvkD43nvvZZyJJBU2exBK\nkqTNYoFQUntav349N9xwA6effjovv/wyAF/60pe4+eabM85MxWjgwIEALFq0iNra2oyzkaTCZYFQ\nkiRtlpkzZwIWCKVSkVJq9NFW+2+uBx54gC996UusWbOGOXPmAHD00UfzwQcftNl7qHz06NGDbbbZ\nho8++oglS5ZknY4kFSwLhJIkqcU++ugjZs+eDcAuu+yScTaSStHhhx8OwNNPP81RRx0FwO67784n\nP/nJLNNSERs0aBDgPISS1BwLhJIkqcVqamr4xje+wYQJE+jdu3fW6UgqQX369OHhhx/m0EMPpXv3\n7gD86U9/4tBDD804MxUrFyqRpE1zkRJJktRivXr14qqrrso6DUklbtGiRey0004ALFu2jIqKCrp2\n7ZpxVipWFggladPsQShJkiSpoJx44om8++673HXXXdx3332ccsopWaekImaBUJI2zR6EkiRJkgrK\n4MGDufvuu7NOQyXCAqEkbZo9CCVJkiRJJStfIHzvvfcyzkSSCpcFQkmSJElSybIHoSRtmgVCSZIk\nSVLJskAoSZtmgVCSJEmSVLLqFwhTShlnI0mFyQKhJEmSJKlkVVRUUFFRQXV1NcuWLcs6HUkqSBYI\nJUmSJEklbdCgQYDDjCWpKRYIJUmSJEklzXkIJal5XbJOQJIkSVLbGzp0KBGRdRodZujQoVmnoAKW\nLxC+9957GWciSYXJAqEkSZJUgubMmZN1ClLB2GGHHejRowdVVVVZpyJJBanNhhhHxIkRMT0iVkXE\nrIg4uF5sbETMyMUej4idGhz7g4hYEhHvR8TVDWJDI+KJiKjKnX9sW+UsSSptEfHLiFgQEcsi4vWI\nOKtebGhE1EbEiohYmfvvZQ2Ot32SJLUp26Zs/OAHP2D16tX8v//3/7JORZIKUpsUCCPiX4DvA6el\nlCqAQ4DZuVh/4H7gMqAf8DJwb71jJwDHAHsBewNHR8TZ9U5/d+6YfsA3gfty5yx7lZWVWafQYbzW\n0uS1qgN8HxieUtqaurbmOxGxX714AvqmlLZKKfVJKX03H7B9alyp/C6XynWA11KISuU6oLSupYDY\nNnWwyspKunbtWhZD7svp/9lyulYor+stp2stJG3Vg3AicGVKaQpASmlBSik/++sJwKsppckppZrc\nvvtExKhc/FTgunrHXAucDpDbZz9gYkqpOqU0GXgFGN9GeRe1cvqfxmstTV6r2ltKaXpKaW3uZVD3\noWvnersETbeFtk+NKJXf5VK5DvBaClGpXAeU1rUUCtumjldOv8dea+kqp+stp2stJK0uEEZEJ+AA\nYLvc0OK5EXFDRHTP7TIamJbfP6W0Gngzt32jeO55PrYHMDulVNVEXJKkZkXETRFRBcwA3gN+Xy+c\ngDm5tuvnDXpZ2D5JktqFbZMkqdC0RQ/C7YGu1H0zdTCwL3XfXH0zF68Aljc4ZgWwVRPxFbltLTlW\nkqRmpZTOoa49+SdgMlCdCy0BPgEMBfanrm25q96htk+SpHZh2yRJKjgppWYfwJNALbC+kcfTwNa5\n+JfqHXMC8HLu+Y+AGxuc82/A8bnny4AD6sX2B5bnnh9H3fDk+sfeAFzfTL7Jhw8fPnxk+9hU29IW\nDzbRPjVxzE+Bc5uIbZ87X++2bp+yvh8+fPjw4cO2ybbJhw8fPgrz0RHtU0seXdiElNJhm9onIuY1\nE34NOK3evr2pm2Pj1XrxfYCXcq/3zW3Lx0ZERO/0967y+wB3NpNv6c88K0lqUfvUiC58fJ6njU7L\n33vXt1n7ZNskSeXBtkmSVKzaapGS24HzImJARGwDfA14KBd7ABgdEcfn5iW8HJiaUpqVi98BXBAR\ngyJiMHBB7nzk9pkKXB4R3SPiBGBP6lZFliSpSbk26YsR0TsiOkXEZ4ETgcdy8U9GxKio0x+4Hngy\npbQydwrbJ0lSm7JtkiQVqk32IGyhq4BtgZnAGuBe4HsAKaUlETEeuIm6b69eoK4RJBe/JSKGUzfs\nOAG3pZRuq3fuE4FJwIfAO8D4lNLSNspbklS6EvAf1A3d6kRdG3J+SunhXHwEdW3VAOrmaPojcPKG\ng22fJEltz7ZJklSQIjf/hCRJkiRJkqQy1FZDjCVJkiRJkiQVoaIrEEbENhHxQESsioi3I+KkZvb9\nz4hYEBHLIuJnEdG1I3NtrZZea0ScFhEfRcSKiFiZ++8hHZ1va0TEORExJSLWRsTPN7Fvsd/XFl1r\nsd/XiOiWuz9zImJ5RPwlIsY1s3/R3tfNudZiv68AEfHLevfq9Yg4q5l9i/a+bo7NaZty+zf5c4mI\nyohYU+93ZEah5L6JvDfrZ9Ae2vBaOvQeNJJbS9v/0RHxh4h4PyLWb+l52lMbXkum9ySXQ0uv5dSI\neCnXHsyNiB9ERKfNPU97acPrKKZ78sVce7U8IhZGxO0RUbG558lCG7cvzZ4rIsZGxIxc/PGI2KlB\n/AcRsST3/+nVDWJDI+KJiKiKiOkRMbZe7NCIWB8f/9vnlM29vkK8tlz85Kj7G3BlREyOiK2byL+o\nr7W5+1ik13plRLwSEesi4tuN5FVK97XJay2l+xp1c7z+KiLmR8SHEfFMRHyywbElcV83da2bc18/\nJutllDf3Adyde/QEDgaWAbs3st9ngQXAbkBf4Enge1nn307XehrwdNb5tvJajwOOoW6uyp83s18p\n3NeWXmtR31egF/BtYMfc6yOpm0tnp1K7r5t5rUV9X3PXsAfQI/d8VO7e7Vdq93UzfyYt+ve6JT+X\n3OszCi33FuTd4p9BEVxLh96DVlzHKOAM4GhgfWt+L4vgWjK9J5t5LRNy8S7ADtStNntxodyXNryO\nYronQ4Dtcs97UTcv+vWFck/a4hpz+27xv9NA/9zrE4BuwA+B5xv8PszI/S7sQN2KyWfXiz8HXAN0\nz53jQ6B/LnYoMLeV97BQr200dX/zHZz73boLuLtEr7XJ+1ik13pK7v0fAL7dIK9Su6/NXWvJ3Fdg\nOHUL5m4HBPBl4H2gV6nd1xZca4vv68euZ3MPyPKRu4nVwM71tk2ikQ+cuZv9nXqvDwMWZH0N7XSt\nRV9wqHctV9F80ayo7+tmXmvJ3Nd61zQNOL6U72sLrrWk7iuwK/Ae8PlyuK9N/Axa/O91S34uuT8s\nziy03JvLe3N/BoV8LR19D1r7+5SL70yDolqx3ZPmriXre9Lanyfwn8D/Vwj3pa2uo5jvCVCR2+93\nhXBP2vIaW/PvNHUfLv/U4L1XA6P+//buPVaOsozj+PfpFVtoaaxFpC1aSsHWxIrGkLQpUqM0aJBb\nApVEsdF4TzAGibFRQ0RMvf1RRY1VEC1SjEoAJRYjVtNKRBsqHFt7C7VYLsEWWtoEm/bxj/c9dM50\nLzO7s2dnZn+fZJM9c3nP+8xvz8zZd3dm4s8bgQ8n5n8I2BSfzyPcqHJyYv4GTryRbfhGNU99Ja7t\nFuBniXlzYl8m17DWTAMOVag11YefcvKgWW1yzVBrLXNNzH+R+EWGuubapNZMuaYfVTvFeB5w1N13\nJaZtIYwEpy2I85LLzTCzaT3sX5Hy1ArwFjN7zsLpEystcQpIzVQ917xqk6uZnQGcS/gYLjvpAAAJ\nO0lEQVTkI61WubapFWqQq5l918wOEz7V2gf8tsFitcq1hbz760bb5YzUdrk1vkb+bGYXFdvdEYo6\nrubdBr3QbS39yiCtqG1ZtUyy6Fcm0F0tSzhxPOh3LkXVMawymZjZIjN7gfDtkSuBb3fSzigr4viS\ndT89Yl13PwLsbDY/te58YLe7H27RzxnxFLxdZvYtM5uUs76y1pZuezdhQGBeqv91qBUa55hWhVrb\nqVOuWdQyVzNbCIyP6zdquza5NqgVsuU6QtXelJ5KOKgnHQROa7Lsi6nlrMmyZZSn1g3Am9x9BnAV\nsBy4sbfd65uq55pHbXI1s3GE03nucPftDRapTa4Zaq1Fru7+SUJui4FfEQ6uabXJtY08++vh5dPb\nhcTynyN8onkW8EPgfjN7QzFdbdiXIo6rebdBL3RbC/Qng0Z9K2JbVi2TdvqZCXRYi5mtAN4KfKOb\ndgpUVB1QsUzcfaO7n07o79eBPZ20M8qKOL5k3U+n1203/2CclmXdbcBCdz8TWEp4LX0zQ5+qUFu7\n+cn+V73WZjk26n/Za22nTrm2s5Ua5mpmU4A7gS+7+6GMbSf7X/Vas+Y6QtUGCF8CpqSmTQUOZVh2\nKuBNli2jzLW6+5Puvic+HwJuBq7ueQ/7o+q5ZlaXXM3MCANmLwOfbrJYLXLNUmtdcgXwYBMwC/h4\ng0XqkuvDZnY8Xug3/fgToc6pqdWaHZugzXZx90fd/bC7H3X3OwmnF1xaaFHN+zLcn7zH1Tzt9Eph\n/yOMcgbt+jbcv7zbsmqZtNTnTKCDWszscsLpTMvcfX+n7RSsqDoqmQmAuz8N/A5Y1007RRjl40u7\nOvPOnxqntV3X3Z91923x+R7C4PJVGX5n6WvLUUPla22RY7v+p/vQatnRqrWdOuXakrs/V7dczewU\n4D7C6birOqih8rXmyHWEqg0QbgfGmdk5iWlvpvFpfENx3rCFwLPufqCH/StSnlobseK7VApVz7Vb\nVcz1R8B04Ep3P+nOlFFdcs1SayNVzDVpHOG6YWm1yNXdL3b3Me4+tsFjCWF/PTbH/jrvdnF69xop\n6rja7TGrCL38H6GXGaQVtS2rlkleo5kJ5KzFwp3sfwC8193/2Wk7PVBUHY2UOpOU8YRvP3bbTldG\n+fjSrs6huDwAZjaZcGx/okXbyXXnxHWy9BPC+9DROgb1srYR68Y+jI99SqpDrY00Gk+oQq3t1CnX\nTlQ2VzObANxLuP7ex1r1q+q5tqm1kfbjf57zooX9fgB3ES4EOYlwatsBmt9NZh/wRmAa4ULKt/S7\n/z2qdRkn7sx2PvA4sLLf/c9Z61jgFOCrhK/HTgTG1jTXrLXWIdfvE+6GNqnNcnXINWutlc4VeA1w\nDTA5HmQuIXzK9Z465ppju2TaX7fbLoRPBt89vF8Arovbd26/+94uzzzboMy19CODLl9PEwnXjToe\nn0+oYiatailDJjlfX0uB54HF3W6TstZRwUzeD8yKz88G/gj8oiyZFFFjXLbj/TThA84DwBUx11Uk\nLoZPuJvmEPA6wmnaQ8BHEvM3xXWG7367nxN3v30HMDs+nxX7tSZnhmWtbT7hLqSLCP8brQXWdvl6\nLWutTXOsaK3jCO/J1hJuHjkRGFPTXFvVWptcY533Ey5/NKZBv2qTa4ZaM+c6Yr12C5TtETf6rwlf\nrXwSuCZR9EFgZmLZG4Bn4otgDTC+3/3vRa2E66g8Q/jHbCfwJRoMOJX5Eft8HDiWeHwx1nqoZrlm\nqrXquQKzY51HYg2H4ut2ed3+XjPUWqdcpxPeWO2PWW0h3sWybrnm3C4N99d5t0vcvn8lXG9kP+Ef\n9aX96HvePFttg37nUPYMOq2DMNCRPJ4cJ1xgvnKZtKqlDJnkrOUPwP/itOHjwW/KkksRdVQwk68A\ne2Md/wa+B0wrSyad1Niozjit4/00YVB4K3A45j87Nf9rwH8JA8e3pubNJrwBPRLbuDgx7zPAU/H3\n7iHcIGZyngzLWlucf22s6xDhzfrp3bxey1prqxwrWuvtnPye7AM1zbVprXXKlXAzrWOx3eR7skV1\ny7VdrXlyTT4sriwiIiIiIiIiIiIDqGrXIBQREREREREREZECaYBQRERERERERERkgGmAUERERERE\nREREZIBpgFBERERERERERGSAaYBQRERERERERERkgGmAUESkIszsajN7wsyOmdkFLZZbZmbbzGy7\nmd2UmL7KzLaa2WNm9kszmxKnn21mR8xsc3zclqEva2I7j5nZPWY2qZgqRUREREREZLRpgFBEpITM\n7CIzuz01+XHgCmBDi/XGAN8BLgEWAMvN7Pw4ez2wwN0XAjuAzydW3enuF8THJzJ08QZ3Xxjb2gt8\nKlNhIiIiIiIiUjoaIBQRKS8f8YP7v9x9B2At1nk7sMPd97j7UeBu4H1x/d+7+/G43CPAzMR6Dds0\ns3eZ2SYz+5uZrRv+pqC7vxTnG/CqdF9FRERERESkOjRAKCJSXq0GAps5i/CNvmFPxWlpK4AHEz+/\nPp5e/LCZLQYws1cDK4F3uvvbgL8Dn32lc2Y/Bp4GzgNWd9BXERERERERKYFx/e6AiIicYGaPABOA\n04BpZrY5zrrJ3R8q6Hd8ATjq7nfFSfuA2e5+IF7b8F4zmw9cCMwHNsZvCo4H/jLcjruviNNXA9cC\ndxTRPxERERERERldGiAUESkRd78QwjUIgQ+6+4qcTfwHmJ34eWacRmz3euBSYGnidx4FDsTnm81s\nFzCP8A3G9e5+XYv+upmtA25EA4QiIiIiIiKVpFOMRUSqqdnpx48Cc+OdiScQvtl3H4S7GxMG8i5z\n95dfachsery5CWY2B5gL7CZcp3CRmZ0T500ys3Pj8+FpBlwGbCu+RBERERERERkNGiAUEakIM7vc\nzPYSTv19wMwejNPPNLMHANz9GOGOwuuBIeBud98am1gNnAo8FK83eFucvgT4Rzyd+R7go+7+grs/\nD1wP/NzMtgCbgPPioOBP4rQtwGuBm3tdv4iIiIiIiPSGuevGkyIiIiIiIiIiIoNK3yAUERERERER\nEREZYBogFBERERERERERGWAaIBQRERERERERERlgGiAUEREREREREREZYBogFBERERERERERGWAa\nIBQRERERERERERlgGiAUEREREREREREZYP8HoRmqvUGuasAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9865f5abd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18,5))\n",
    "\n",
    "ax1 = fig.add_subplot(131)\n",
    "ax1.plot(potrho_GS.values, -z_t.values, 'k', lw=2)\n",
    "ax1.set_title('Potential density', fontsize=14, y=1.03)\n",
    "# ax1.set_xlabel('lon', fontsize=12)\n",
    "# ax1.set_ylabel('lat', fontsize=12)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "ax2 = fig.add_subplot(132)\n",
    "ax2.plot(u_ACC.values, -z_t.values, 'k', lw=2, label=r'$u$')\n",
    "ax2.plot(v_ACC.values, -z_t.values, 'k--', lw=2, label=r'$v$')\n",
    "ax2.set_title('Horizontal velocities', fontsize=14, y=1.03)\n",
    "# ax2.set_xlabel('lon', fontsize=12)\n",
    "# ax2.set_ylabel('lat',fontsize=12)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.legend(loc='lower right', fontsize=14)\n",
    "\n",
    "ax3 = fig.add_subplot(133)\n",
    "ax3.plot(N2_ACC.values, zN2_ACC.values, 'k', lw=2)\n",
    "ax3.set_title(r'$N^2$', fontsize=16, y=1.03)\n",
    "# ax1.set_xlabel('lon', fontsize=12)\n",
    "# ax1.set_ylabel('lat', fontsize=12)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zphi, Rd_ACC, vd = baroclinic.neutral_modes_from_N2_profile(-zN2_ACC.values, \n",
    "                                                        N2_ACC.values, f0_meta.sel(Latitude_t=ACC[1]).values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.savez('OCCA_ACC',\n",
    "        absolute_salinity=absS, conservative_temperature=consT,\n",
    "        potential_density=potrho_meta, \n",
    "        z_N2=zN2_meta, N2=N2_meta,\n",
    "        u_at_Tpoints=u_coinT, v_at_Tpoints=v_coinT\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[            nan  17723.58428203   8362.66058973   5565.62704798\n",
      "   4140.69532059   3297.01669271] (44,) [ 0.15075567  0.15075567  0.15075567  0.15075567  0.15075567  0.15075567\n",
      "  0.15075567  0.15075567  0.15075567  0.15075567  0.15075567  0.15075567\n",
      "  0.15075567  0.15075567  0.15075567  0.15075567  0.15075567  0.15075567\n",
      "  0.15075567  0.15075567  0.15075567  0.15075567  0.15075567  0.15075567\n",
      "  0.15075567  0.15075567  0.15075567  0.15075567  0.15075567  0.15075567\n",
      "  0.15075567  0.15075567  0.15075567  0.15075567  0.15075567  0.15075567\n",
      "  0.15075567  0.15075567  0.15075567  0.15075567  0.15075567  0.15075567\n",
      "  0.15075567  0.15075567] [ 0.20222925  0.20221197  0.20218506  0.20209236  0.20183898  0.2013884\n",
      "  0.20068079  0.19992397  0.19926578  0.19869092  0.19816759  0.19765314\n",
      "  0.19712988  0.1965532   0.19590214  0.19503075  0.19390261  0.19216506\n",
      "  0.18926727  0.1848589   0.17860189  0.17035836  0.16001848  0.14743217\n",
      "  0.1326062   0.11582929  0.09762903  0.07867682  0.05962827  0.04104523\n",
      "  0.02343003  0.00711188 -0.007833   -0.02155083 -0.03429478 -0.04622199\n",
      " -0.05730543 -0.06730798 -0.07596724 -0.08342703 -0.09133966 -0.09210186\n",
      " -0.09202907 -0.09219879]\n"
     ]
    },
    {
     "data": {
      "image/png": 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f1q3rarZav15Z/3zRIjj1VEUgh7juiSqL5KHKYoSyverPlJXfy4cso4Eciuzj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\nJgsWxhhjYrJgYYwxJqb/DyrYsXz5YdliAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f98663fd090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print Rd_ACC, zphi.shape, vd[:, 0], vd[:, 1]\n",
    "plt.figure()\n",
    "for i in range(vd.shape[1]):\n",
    "    plt.plot(vd[:, i], -zphi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dx_ACC = gsw.earth.distance([ACC[0]-.5,ACC[0]+.5], [ACC[1],ACC[1]])[0][0]\n",
    "dy_ACC = gsw.earth.distance([ACC[0],ACC[0]], [ACC[1]-.5,ACC[1]+.5])[0][0]\n",
    "dx = 6e2; dy = 6e2\n",
    "Nx_ACC = 100\n",
    "Ny_ACC = 100\n",
    "k = fft.fftshift( fft.fftfreq(Nx_ACC, dx) )\n",
    "l = fft.fftshift( fft.fftfreq(Ny_ACC, dy) )\n",
    "\n",
    "k_ACC = k[np.absolute(k) < 5.*Rd_ACC[1]**-1]\n",
    "l_ACC = l[np.absolute(l) < 5.*Rd_ACC[1]**-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -8.33333333e-04  -8.16666667e-04  -8.00000000e-04  -7.83333333e-04\n",
      "  -7.66666667e-04  -7.50000000e-04  -7.33333333e-04  -7.16666667e-04\n",
      "  -7.00000000e-04  -6.83333333e-04  -6.66666667e-04  -6.50000000e-04\n",
      "  -6.33333333e-04  -6.16666667e-04  -6.00000000e-04  -5.83333333e-04\n",
      "  -5.66666667e-04  -5.50000000e-04  -5.33333333e-04  -5.16666667e-04\n",
      "  -5.00000000e-04  -4.83333333e-04  -4.66666667e-04  -4.50000000e-04\n",
      "  -4.33333333e-04  -4.16666667e-04  -4.00000000e-04  -3.83333333e-04\n",
      "  -3.66666667e-04  -3.50000000e-04  -3.33333333e-04  -3.16666667e-04\n",
      "  -3.00000000e-04  -2.83333333e-04  -2.66666667e-04  -2.50000000e-04\n",
      "  -2.33333333e-04  -2.16666667e-04  -2.00000000e-04  -1.83333333e-04\n",
      "  -1.66666667e-04  -1.50000000e-04  -1.33333333e-04  -1.16666667e-04\n",
      "  -1.00000000e-04  -8.33333333e-05  -6.66666667e-05  -5.00000000e-05\n",
      "  -3.33333333e-05  -1.66666667e-05   0.00000000e+00   1.66666667e-05\n",
      "   3.33333333e-05   5.00000000e-05   6.66666667e-05   8.33333333e-05\n",
      "   1.00000000e-04   1.16666667e-04   1.33333333e-04   1.50000000e-04\n",
      "   1.66666667e-04   1.83333333e-04   2.00000000e-04   2.16666667e-04\n",
      "   2.33333333e-04   2.50000000e-04   2.66666667e-04   2.83333333e-04\n",
      "   3.00000000e-04   3.16666667e-04   3.33333333e-04   3.50000000e-04\n",
      "   3.66666667e-04   3.83333333e-04   4.00000000e-04   4.16666667e-04\n",
      "   4.33333333e-04   4.50000000e-04   4.66666667e-04   4.83333333e-04\n",
      "   5.00000000e-04   5.16666667e-04   5.33333333e-04   5.50000000e-04\n",
      "   5.66666667e-04   5.83333333e-04   6.00000000e-04   6.16666667e-04\n",
      "   6.33333333e-04   6.50000000e-04   6.66666667e-04   6.83333333e-04\n",
      "   7.00000000e-04   7.16666667e-04   7.33333333e-04   7.50000000e-04\n",
      "   7.66666667e-04   7.83333333e-04   8.00000000e-04   8.16666667e-04] 0.000282109979586\n",
      "(33,)\n"
     ]
    }
   ],
   "source": [
    "print k, 5.*Rd_ACC[1]**-1\n",
    "print k_ACC.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### w/out lateral viscosity ($A_h=0$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "zpsi, w, psi = baroclinic.instability_analysis_from_N2_profile( -zN2_ACC.values, \n",
    "                                                                   N2_ACC.values, f0_meta.sel(Latitude_t=ACC[1]).values,\n",
    "                                                                   beta_meta.sel(Latitude_t=ACC[1]).values,\n",
    "                                                                   k_ACC, l_ACC, z_t.values, u_ACC.values, v_ACC.values, etax, etay,\n",
    "                                                                   Ah=0., num=2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f98655532d0>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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c/SKc0TtaLZg3Ew6rTe7s71C8WeOKSF42/cg66eVf1uHNV69z5/VdDt7cotqb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\nwxggJr5hDJD/DysOfUpIyMsmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9865b112d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sig = w[0].copy()\n",
    "for i in range(13):\n",
    "    sig[:, i] = np.nan\n",
    "for j in range(13):\n",
    "    sig[j, :] = np.nan\n",
    "\n",
    "for i in range(-1,-13,-1):\n",
    "    sig[:, i] = np.nan\n",
    "for j in range(-1,-13,-1):\n",
    "    sig[j, :] = np.nan\n",
    "\n",
    "plt.imshow(sig.imag, origin='bottom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ngCWSje98J9jc4s47Ye218x6N5EEBa/XXsg9YDVkHLYWslsoStIpUzVLIak8BS4pAAUuk\n++6+G/7lX+C3v4Xttst7NJKXngtYA8k1YDUoaHVMQas61ayqhywFLCkCBSyR7vrzn2H33WHaNBg/\nPu/RSJ56aRfBcsh610HtNNhSN3YahPTvnZXWToNp7IKYpH2i+2XtTvxdBnO4V1ZRmNl+ZvaQmT1i\nZqf3c840M1tkZveZ2bvbtTWzYWY228weNrNbzGyjptcmhX0tNLN9mo7vZmb3h31N7XP9Q83sD2a2\nwMx+1Oe1oWb2hJlNS+P9EBFJYsUKOPzw4KFwJWlRBatbsqxoqZrVkqpZ2bbPpZrV5UpW0SpYZrYW\n8AiwF/A0MA84zN0fajpnf+BEdz/QzPYAvu3u4wZqa2ZTgL+4+/lh8Brm7meY2c7AlcB7gJHAbcD2\n7u5mNje8zjwzmxVe5xYz2w64GtjT3V82s03d/bmm8U0FNgWed/eTYrw1PUcVLJHumTQpmB54yy0w\naFDeo5G8qYJVdFlWtLp976w5dK2i9eqcYV29d1ZX+i1wNStp+6TVrNhUyerUWGCRuz/u7q8DM4G+\nv3MdD8wAcPe5wEZmNrxN2/HA9PD5dOCQ8PnBwEx3X+7ui4FFwFgzGwEMdfd54Xkzmtp8Fviuu78c\njqE5XI0BNgNmJ3sbRESSu+IKuOYamDlT4UrSpYDVbVULWl3S6zcons+uqQStIkwZjE0hqxObA080\nff1keKyTcwZqO9zdlwK4+xKCENSqr6ea+nqyn75GATuY2W/M7Ldmti+AmRnwLeA0QGvLRCRXt98O\nZ5wBN94Ib3lL3qORqlHAykrWQatbSlzNKkvQSoNCVofGUNagFUWcMJNkPtogYDvgA8DhwH+b2YbA\nROBGd386wbhERBJ78EGYMAGuvlo3E5buUEE0a42Q1e01Wo2Q1a31WY2Q1YX1Wa/OGda1tVkLXh7d\nlbVZCxid2tqsRshKujYr6ZgaIStOH42QFWtd1u7EW5PV+LsY5xcAY4h9r6xObN1BXq3/Fep/azrw\nbMvTngK2bPp6ZHis7zlbtDhn8ABtl5jZcHdfGk7/a1y9v776Ow5BNWuOu78BLDazR4DtgfcC7zOz\niQQ3jV/HzJa5+5ktv1MRkS5YujS4kfA3vwm1Wt6jkaqqziYXhMvIh26d61giy2ozjG5uhKFNMFbv\nN8VNMKqwAUbszS8KsI17WptceIyCYKtrm9nawMMEG1U8A9wNTHD3hU3nHACcEG5yMQ6YGm5y0W/b\ncJOL5919Sj+bXOxBMAXwVlZtcjEHOIlgs4wbgWnufnM4JXCCux9lZpsSvKvvdvcXmsZ4JDBGm1x0\nRptciKRj2TLYay/Yf384++y8RyNFpPtgNVktYPWnyMGrCiELFLSa+yzgToOlDFmQ6w6DRQtYYX/7\nAd8mmOJ9mbufZ2bHA+7u3w/PuQjYD/gbcLS739tf2/D4JsA1BFWpx4FD3f3F8LVJwLHA68DJ7j47\nPD4G+CGwHjDL3U9uGuN/hddfDnzN3X/S53tQwIpAAUskuddeCypXb387XHopmCYpSwsKWE06Clh9\nFTFwVSFoKWSt3m/BqlkKWR0KQ1YRA5b0HgUskWRWrIBDD4W11gp2DFx77bxHJEWlgNUkVsDqq0iB\nS0FrQApa5Q1aZQtZClhSBApYIvG5w2c/C3/6E9xwA6y7bt4jkiJTwGqSSsBqVpSwpaA1oLIFLYWs\nVTJflxUzZNkPFbAkfwpYIvGdfjrU6/DLX8IGG+Q9Gik6BawmqQesvvIOXFkELYWsNaia1d22ZQhZ\nClhSBApYIvGcfz5Mnw533AFvfnPeo5EyUMBq0vWA1azqYUtBaw1FD1oKWRFEDFkKWFIEClgi0X3n\nO3DhhUG4Gjky79FIWShgNck0YDXLM2wpaLVUtqBVpGqWQtaaFLCkCBSwRKL5/vfh618PpgZuvXXe\no5EyUcBqklvAapZX2Cpr0CppyIJiB608Q1aS9kUNWQpYUgQKWCKd++EP4atfDcLVttvmPRopGwWs\nJoUIWA0KWtGUNGhVOWRBPtWsIoYsBSwpAgUskc5cdRV8+ctw++2www55j0bKSAGrSaECVrOsw1ZZ\nQxYoaDX3WZCgVaqQBfGCVpuQpYAlnTCzs4DPAs+Gh85095vD1yYBxxDcdLn5Rs27sfqNmk8ZoH8F\nLJE2rr0WvvAFuO022GWXvEcjZaWA1aSwAatBQaszJQ1ZkH7Q6uWQBRlXswYIWQpY0okwYC1z9wv6\nHN8JuAp4DzASuA3Y3t3dzOYCJ7r7PDObBXzb3W/pp38FLJEBXHcdHHcczJ4N73pX3qORMksrYK2V\nxmCkjWWLVz2yMDd8dMud4SNtc4h/U9g2Xp0zjFfnDOtO58CCl2N8gh6oP0azgOR9zmdX5rNrLuNI\nMv4XxgyJ13D3GG26GOylp7T6gTwemOnuy919MbAIGGtmI4Ch7j4vPG8GcEg2wxSplhtuCG4kfOON\nCldSHApYWcsybM2lu2FLQWs1C14e3ZWglYYkISvJOJIEtMxDloKWJHOimd1nZj8ws43CY5sDTzSd\n81R4bHPgyabjT4bHRCSC66+Hz3wmCFe7x/m3X6RLBuU9gJ7WCFlZTCFshKxuTB+8k+5MG2yErC58\n8G2ErG5MHWyErLSmDTYCStJpg/PZNdGUwQWMjj2GuG1fGDMk3nTB3Yk3XXAcXQv3Um5mdiswvPkQ\n4MC/AxcD54RT/74G/BfwmTSvP3ny5JXPa7UatVotze5FSud//xeOP17hSpKp1+vU6/XU+9UarKLJ\nar1Wt9ZpaX3WGoq4PitJ0CrV5hcJdxhMbQ3WUTHapXBtyZ6ZbQXc4O7vNLMzAHf3KeFrNwNnAY8D\nv3L3ncLjhwEfdPfP99On1mCJNPn5z+Fzn4ObboLddst7NFIlWoNVVVlNIezW1EFNG1xDEacNJl2X\nlXXbTKcLgqYLSiThmqqGjwIPhM+vBw4zs8Fmtg2wHXC3uy8BXjKzsWZmwBHAdZkOWqSkfvpThSsp\nPlWwyiCLqpYqWiuVaVv3pNWsvHYZLEslyz6nCpa0Z2YzgHcDbwCLgePdfWn42iTgWOB1Vt+mfQyr\nb9N+8gD9q4IlAvzkJ8FW7DfdBLsmW1os0pK2aW9S+YDVoKC1ui5XGcoStMo6ZbAMIUsBS4pAAUsE\nrrkGTjoJbrlFuwVK92iKYC/KYvpgN6cOpq2L0wah+zsOptZXSacMlma6oIiI5Oqqq+DkkxWupDxU\nwSq7ble1ulHR0rTB1RSpmlWmzS+yqGSpgiVFoAqW9LLLL4evfjW4ifAuu+Q9Gqk6TRFs0tMBq6Gb\nQUvTBlcqQ9DKc8pgaXYY7DBkKWBJEShgSa+65BL4+tfhl7+EUaPyHo30AgWsJgpYfZQtbClordTr\n1ayihSwFLCkCBSzpRRdeCNOmBeHq7W/PezTSKxSwmmQTsOoRzq11aQwRlSlolXDaIBQ/aClktdEm\nZClgSREoYEmvOe88+MEP4PbbYcst8x6N9BIFrCbdDVj1hO1rKYwhBd0KWwpaQLWDVi+HLAUsKQIF\nLOkV7nD22XD11UHl6m1vy3tE0mu0i2Am6in10fzISbd2H0x7x8Fu3qi4i7q142Bauw0m2WlwPrvG\n3mWwFDsMlmx3QTPbz8weMrNHzOz0fs6ZZmaLzOw+M3t3u7ZmNszMZpvZw2Z2i5lt1PTapLCvhWa2\nT9Px3czs/rCvqU3HjzSzZ83s3vBxTNNrW4T9P2hmD5iZfjctIkAQriZNgp/9DH79a4UrKTdVsFqq\np9hXO7UMr9WkGxUtVbNW6kZFK41qVl5TBstaySpaBcvM1gIeAfYCngbmAYe5+0NN5+wPnOjuB5rZ\nHsC33X3cQG3NbArwF3c/Pwxew9z9DDPbGbgSeA8wErgN2N7d3czmhteZZ2azwuvcYmZHAmPc/aQW\n78WvgHPd/XYzexPwhrv/Pfq701tUwZKqc4cvfhHuvDPYLfDNb857RNKrVMHqmnoO12t+ZKQbFa20\n76HVzWpWBhWttKVRzVrA6MTVrLjXzbJdhStZY4FF7v64u78OzATG9zlnPDADwN3nAhuZ2fA2bccD\n08Pn04FDwucHAzPdfbm7LwYWAWPNbAQw1N3nhefNaGoDsMYPJzPbCVjb3W8Px/aKwpWIrFgBxx0H\nc+cG0wIVrqQKFLBWU897AGQethS0uqYb0wYXvDw6taAVl0JWrjYHnmj6+snwWCfnDNR2uLsvBXD3\nJcBm/fT1VFNfTw4wjo+a2e/N7BozaxwfBbxkZj81s3vMbIqZaY2ZSA9bvhyOOAIefTSoXG28cd4j\nEklHdQJWt2+4m4s6mQWuRtBKM2x1I2h1QwnXZ6URtPIKWXGuq5CVSJwQk2Q+2vXA1u7+LoIphTPC\n44OA9wGnEkw53BY4KsF1RKTEXnsNDj0UXngBZs2CoUPzHpFIegblPYBUDd06YUCoUYwqVn/qTc9r\n3btM4z1MK7Q2QlYaa7QaISvt9VmNkNXF9VmNkJXm+qwFL49OtDarEVzirHVqhKw467IWMDryNeO0\ngSBkRV6TlXbI6uDvVf1hqD/S9rSngOaNIUaGx/qes0WLcwYP0HaJmQ1396Xh9L9n2/TV33Hcvfkv\n+A+AKeHzJ4H73P1xADP7X4J/Fa4Y4PsVkQp65RX42MfgTW+Cn/8c1l037xGJpKs6FayGSlayWqmT\nWVUrLZo2CKRf0SpzNSuLNhCzkpWx2g4w+aBVj37MA7Yzs63MbDBwGEHFqNn1wBEAZjYOeDGc/jdQ\n2+tZVU06Eriu6fhhZjbYzLYBtgPuDqcRvmRmY8Npfkc02oQBrWE8sLBp7BubWWOFxYeABzt4a0Sk\nQpYtgwMOgE03DbZjV7iSKqpewIKEIauW0iCyUqdUQatM0wZLGLQStVfIKjx3XwGcCMwG/kCwAcVC\nMzvezI4Lz5kFPGZmjwKXAhMHaht2PQXY28weJthl8LywzYPANQRBaBYwsWk7uxOAywh2Jlzk7jeH\nx08Kt2CfH17vqLCvN4DTgNvN7Pfhuf+d5vsjIsX2wguw996www4wfToMqtY8KpGVqrNN+9AW30ei\nUFBP0DZPte5fIs0qYZpbu3drW3co1Y2Kk27nnsdNibPcxj3KdEHbPaVt2r8Xo10KW8RLNWibdqmC\nZ5+FffaBPfeECy4AbXEjRZTWNu3VDliQIGTVY7Yrklp3uy9i0OpmyAIFrQ5UKWQpYEkRKGBJ2T39\nNOy1F3z843DOOQpXUlwKWE26E7CgGiGroda9rtMKWmWpZkFpgpZCVv86CVkKWFIEClhSZosXB+Hq\ns5+FM87IezQiA1PAajJgwAKFrDXUutOtglbqihC0ejlkKWBJEShgSVk9/HCw5ur00+GEE/IejUh7\naQWsam5y0VfP7CzYqTpd2Rwjrc0w0twIo1ubYDRkcLPiNCTZACPuphJluCFxFTa+EBEpovvvD9Zb\nnXOOwpX0nt6oYDX09Hqsdmrpd5lGsC1TNQu6WtFKo5pVpimDRahkqYIlRaAKlpTN3XfDQQfBRRfB\nJz6R92hEOqcpgk06DligkNVWLf0uixS0sghZUOmg1UshSwFLikABS8rk178OQtUVV8CBB+Y9GpFo\nNEUwrtgf9mspDqLI6hRy6mCaUwa7PW0QVk0d7ML0wTSmDZZlyqCmC4qIlMfNNwfhauZMhSvpbdUJ\nWFEqHApZHahTuKCV9tqsLIIWdCVopXGTYoWsNSlkiYjE87OfwZFHwnXXwYc+lPdoRPJVnSmCHw6/\njygfwDVdMIJa+l0mmTqY5tosyG7qIHRl+mDSaYNxpwxWfbqgpghKEWiKoBTdj34EX/4yzJoFu8bb\n30ikEHpiiqCZjTSz283sD2a2wMxOatsok0pWL6pTqIpWmtUsyK6aBV2raCURt5qlSpaISG+79FKY\nNAluv13hSqSh0BUsMxsBjHD3+8xsA+AeYLy7P9TnvFUVrIauV7LqMdpUTS29rnq1mgWpVrRUyUq3\n3Sb2qipYkjtVsKSoLrwQpk2D226DbbfNezQiyfVEBcvdl7j7feHzvwILgc07atz1SlYtRpuqqZNa\n0CxaNaukFa2ka7NUyRIRkXbc4Wtfg0sugTvuULgS6avQFaxmZrY1waf5d4Rhq/m1NStYDapkZaiW\nTjdFqmb5lyU2AAAgAElEQVQ1lHCNVpmqWUWtZKVWwfpdjHYprP+SalAFS4rEHc48E37xC7j1Vhgx\nIu8RiaSnp+6DFU4PrAPnuvt1LV7vP2CBQlbmaul0o6CViiRBq9dDlgKWFIEClhTFG2/AKafAb38L\nt9wCb35z3iMSSVfPBCwzGwT8ArjJ3b/dzznONmetOjCsBpvUVj9JISsHtXS6iRu0uhWyoHRBSyGr\ns3a/qa/gN/U3Vn59/tnLFbAkdwpYUgQrVsBxx8FDDwW7BW60Ud4jEklfLwWsGcBz7n7qAOcMXMFq\nUMjKSS15F0UMWQ1ZhC2FrNSv00kbVbCkCBSwJG+vvx7c42rp0uA+VxtskPeIRLqjJwKWmf0zcAew\nAPDwcaa739znvM4CFihk5aqWvAsFrcTiBq1eDFkKWFIECliSp9deg09+MghZ114LQ3QXC6mwnghY\nnYoUsEAhK3e1ZM2LHLIauh22EgYthazO2ihgSREoYEleXnkFPvIRGDoUrroKBg/Oe0Qi3dUT27R3\njbZwz1mdRCE0yXbuWbmT7m73nnBb97hbuWe5jXtWW7hr+3YRkTUtWwYHHADDh8PMmQpXIlH0ZgWr\nQZWsgqjFa1bEXQYH0q2qVg7VrF6pZKmCJUWgCpZk7aWXYP/9YZdd4NJLYa3e/HW89CBNEWxiZs7Z\nHq9aoJBVILV4zcowZbCvtMNWiaYMlilkKWBJEShgSZaefx723RfGjYNvf1vhSnqLpgi2EudDa9Tp\ngpE/zNcint/L6vGaJZkymOW0wWZpTx+cQ6Jpg1lOGdR0QRGRYvrzn2HPPaFWg2nTFK5E4qre/zrd\nDlmgkNVVdWIFrbghC4oRtNIKWwpZia9T9JBlZvuZ2UNm9oiZnd7POdPMbJGZ3Wdm727X1syGmdls\nM3vYzG4xs42aXpsU9rXQzPZpOr6bmd0f9jW1xRg+ZmZvmNluTcemmNkDZvaHVm2Kzsw+Ho5/RfP3\nFb4W6X0ys8FmNjNsc5eZbZnl9yLS1zPPBMHq4IPh/PPBVD8Xia16AQsUsiqhHr1JkpAF+YWshrSC\nVoJqlkJW/DZZMLO1gIuAfYFdgAlmtmOfc/YHtnX37YHjge910PYM4DZ33wG4HZgUttkZOBTYCdgf\nuNhs5ceuS4Bj3X0UMMrM9m0awwbASTT9TTSz9wL/5O7vAN4BjDWzD6TyxmRnAfAR4NfNB81sJ6K/\nT8cCz4f/naYC52cwfpGWnngCPvhBmDABzj1X4UokqWoGLFDIqoQ6kYNWGiErz4oWpBu0YlDIit8m\nA2OBRe7+uLu/DswExvc5ZzwwA8Dd5wIbmdnwNm3HA9PD59OBQ8LnBwMz3X25uy8GFhEEoxHAUHef\nF543o6kNwLnAecBrTcccWM/M1gOGAIOApfHehny4+8Puvgjo+/FzPNHfp+b3/Fpgr64OXqQfjz0W\nhKvjjoOvfCXv0YhUQ3UDFihkVUY92ulJQ1ZD3mErjaClkJXoOgW0OfBE09dPhsc6OWegtsPdfSmA\nuy8BNuunr6ea+nqyVV/h1LmR7n5T86DcfQ7B/8zPhP3c4u4PD/jdlkfk96m5jbuvAF40s026P1SR\nVRYtCqYFnnoqnHZa3qMRqY5qByxQyKqMOpGCVlohqyHPsJU0aMWcMqiQVRlxJvvE2rIunBb3X8CX\n+l7fzLYFdgTeRhAu9jKzf45znW4ys1vDNVONx4Lwz4O6feku9y+ymoULgw0tvvIVOPHEvEcjUi2D\n8h5AJt5P9A+oexDtw/TQrSN+qK+hLdzjqNNxQF22ONm9svrT9+9FVtu930my7d3nEHk790bIirqN\n+4KXR0fewn0BoyNvrT6fXSNv4R7nOkm9MGZI23N+U1/Bb+pvNB1Z3uq0p4DmzRBGhsf6nrNFi3MG\nD9B2iZkNd/el4bS2Z9v01d/xoQTrq+ph2BoBXGdmBwMfAua4+6sAZnYT8F7g/1p9o3lx971jNIv6\nPjW3edrM1gY2dPfn+7vA5MmTVz6v1WrUarUYwxQJPPAA7LMPnHceHHFE3qMRyU+9Xqder6feb7Xu\ng9VOt++TBTEqJ/WI50ug1vmp3QhZ7XQ7dCUJWjHvmZXVDYmLdJ+sD9rdqdwH63lvH7D6anUPrvCD\n+MME63WeAe4GJrj7wqZzDgBOcPcDzWwcMNXdxw3U1symEGy4MCXcXXCYu58RbnJxJcHf6M2BW4Ht\n3d3NbA7BRhbzgBuBae5+c5/x/go41d3nm9mhwGcINoFYC7gJuNDdb4z85uQs/L5Oc/d7wq8jv09m\nNhF4h7tPNLPDgEPc/bB+rqf7YElq7r8/uM/VBRcEm1qIyCq6D1Ycmi5YIXU6DqdpTxfsRLenFCaZ\nNpjhuixNF0xXuFbnRGA28AeCjRUWmtnxZnZceM4s4DEzexS4FJg4UNuw6ynA3mbWCGDnhW0eBK4B\nHgRmARObPumfAFwGPEKwecZq4aoxZFZNfbsW+H8EO/HNB+aXLVyZ2SFm9gTBryl+EVbh4r5PlwGb\nmtki4BSCnRxFuuq++4LK1dSpClci3dRbFawGVbIqptbZaXlUslpJu7oVt5qlSlbb6xStgiW9SRUs\nScO998IBB8B3vgOf+ETeoxEpJlWwkihsJasWsY0E6p2dlkclq5W0q1tJKlkZbX5R5EqWiEjV3XMP\n7L8/XHyxwpVIFnozYEF2IUtTBjNS7+y0ZYuLE7QgvaCV8ZTBIoesIl5DRCQv8+YFlatLL4WPfjTv\n0Yj0ht4NWJBNyAKFrMzUOz+1SCEL0qtqVTBkRdUr67FERNqZOxcOPBB+8AM45JD254tIOqoTsGKu\nJ1HIqpp656cWLWQ1JA1aBQ9ZUWnTCxGR6O66Cw46CK64IvhTRLJTnYAFClkSqnd+atGmDDZLErQK\nHLKKvB5LIUtEquC3v4Xx42H69KCCJSLZqlbAgvghKw6FrOooasiCZCErTtBSyIrcRkSkKP7v/4Lp\ngP/zP8HGFiKSveps035Ln+8jzr1+4v7WP84HYG3jnoFatpfLYhv4uFu8x6nSRvxlRRW3b9c27VIE\n2qZdOnXXXUHl6kc/Cu53JSLRaJv2duJUsuLeT0iVrIKqZ3u5xnTDvo80ZTllMOIvKYq8s6AqWSJS\ndXffvWpaoMKVSL6qG7BAIUsoROUv7cAVd22WQlbkNiIiZXDPPcFGFpdfrmmBIkVQ7YAVV+FDVi3G\nhaQw0gxaUSlkRW4jIlJk99236j5X//IveY9GRKAXAlaWOwtCRiELFLKiqOc9gNbSCFoKWZHbKGSJ\nSFXcfz/stx9cfLHucyVSJNUPWKCQJRQ2ZEHyoJVVyIqoyCFLRKTsHngA9t0Xpk2Dj30s79GISLPq\n7iLYSpydBaHguwtCocND4dTyHkB7cXcjjBPso/4SIcYvK7LYXbBbOwueaJelsovgr31s5HZp7GAo\n1aBdBKWvhQthr73gW9+Cww/PezQi1aFdBOOodCWrFqNdL6rnPYD24lazsqhkZXCPrDg0VbB3mdkd\nHT5m5z1WkTQ8/DB8+MMwZYrClUhR9VYFq6EMlSxQNavrankPoL04gbsilawi3CNLFaziM7NXgc+1\nOw34trtvlMGQUqcKljQ8+ijsuSeccw4cfXTeoxGpnrQqWL0ZsEAhS5rUIpxbT6mfCBSyorVJMWQp\nYBWfmf3S3ffq4LzZ7l7KuwMpYAnAn/4EH/gAnHkmHHdc3qMRqSZNEUwq7nTBrGnziwzU6T841fs8\nOumn0/M7FCdkF3S6YFTaWVDa6SRcheeVMlyJACxZEkwLPPlkhSuRMujdgAXFvxFxg0JWTuop9ZFC\nP1mFrKi0fbuISFc9/zzssw986lPwxS/mPRoR6URlAlacncpiK1XIqiW4qKSnnryLLEKW7pEVuY0U\nh5m9y8xuN7Pnzewf4eN1M/tH3mMTiePll4P7XO27L3z1q3mPRkQ6VZk1WENeeh6IuWNZWdZjQYL7\nJdUTXLQIahHOrad0nST9dNJ/TFmsySrgeizIfk2W1mCVi5k9CPwUuBp4tfk1d/9jLoNKgdZg9aZX\nXoH994eddw5uJGz610Ck67QGK01l2L49sVoeF01Jrcvnt1JPoY/++k3Yd5KbEneqoNu3x6lkSU8Z\nAfyHuz/g7n9sfuQ9MJEoXnsNPvpR2Gor+O53Fa5EyqZyASv2VMGyhKy4N6EFyjllsJZxu6z6ridr\nHjVkFXTTiyLfI0vTBUtpOqA7A0mpLV8OEybA+uvD5ZfDWpX7pCZSfZX831Yhq51awvZZqeU9gCY1\nVg+otX7Oi6KeQh8RFHDTiziyWo9VVGa2n5k9ZGaPmNnp/ZwzzcwWmdl9Zvbudm3NbJiZzTazh83s\nFjPbqOm1SWFfC81sn6bju5nZ/WFfU1uM4WNm9oaZ7dZ07Mjw/IfN7Ig03o8+zgPONbM/hGuxVj66\ncC2R1L3xBhxzDLz6Klx1FQwalPeIRCSOSgYsyCFkxaWQ1Y9aTn3UW/QRp5+414tAm15Ea1OBkGVm\nawEXAfsCuwATzGzHPufsD2zr7tsDxwPf66DtGcBt7r4DcDswKWyzM3AosBOwP3Cx2crJSpcAx7r7\nKGCUme3bNIYNgJNo+ttgZsOA/wDeQ/Av31nNQS4l1wKPhWO7ss9DpNDc4YQT4PHH4ac/hXXXzXtE\nIhJXZQMWZByy4laxkqhsyKrl3FedzoJVnL77u15MRQ1ZESlkdWwssMjdH3f314GZwPg+54wHZgC4\n+1xgIzMb3qbteILpdYR/HhI+PxiY6e7L3X0xsAgYa2YjgKHuPi88b0ZTG4BzCapJrzUd2xeY7e4v\nufuLwGxgv5jvQ3/eDezv7he5+2XNj5SvI5K6r3wFfvc7uOEGeNOb8h6NiCRR6YAF2r69vVrC9mVQ\ni3hu1PPTUI/fVJteRGtT7pC1OfBE09dPhsc6OWegtsPdfSmAuy8BNuunr6ea+nqyVV/hlMCR7n5T\nm3E1+krTncDOKfcp0nXTpsG118KsWbDhhnmPRkSSqnzAgpghqyzrsaBiIavW+vDQrVP4PrullvcA\noqvQphc9GLKiirP/WKw9wcPpg/8FfClO+xQ8Bsw2s0vN7JzmR07jEWlr5kz45jdh9mx4y1vyHo2I\npEHLJwcyjniL9t9PvGlVexB/I4KhWyesZNQoxb2yGiGrK1WbWhf6jKIefwzLFkcPoHPp/i0D5hD5\nlxWvzhmWbeU5A50Eukfqz7Co/ky7054Ctmz6emR4rO85W7Q4Z/AAbZeY2XB3XxpO/3u2TV/9HR8K\nvAOoh2FrBHC9mR0cvl7r0+ZXbb7fqN4E3EjwvTaPTzeRkkK69VY4+WS47bZgS3YRqYbK3Wh4ILG3\nhC7LjYhTCR31FPqIq9b6cLvg0NH3XY9//Y51co0ujiGLGxBDvCptxJBVlJsQX2YnpnKj4Yv82Mjt\nWt3k2MzWBh4G9gKeAe4GJrj7wqZzDgBOcPcDzWwcMNXdxw3U1symAM+7+5Rwd8Fh7n5GuMnFlQR/\nUzYHbgW2d3c3szkEG1nMIwg109z95j7j/RVwqrvPDze5+B2wG8Hsid8BY8L1WDIA3Wi4mn73u+BG\nwj/7Gbw/j3XcIrIG3Wg4htLsLBhXKlPoain0kaJOvqdUpg/WErZPq496/KZZbHgB2vQiR+6+AjiR\nYIOIPxBsQLHQzI43s+PCc2YBj5nZo8ClwMSB2oZdTwH2NrNGADsvbPMgcA3wIDALmNj0Sf8E4DLg\nEYLNM1YLV40hE05RdPcXCDa/+B3B37yz0whXZjYkzfNEsrBoERx0EPzgBwpXIlWUqIJlZvcQ/KC+\nFbgJWB/YJfwBn5lOK1gNsSpZZaliQUkrWbXWh6MGp36/93q86/fVPJ6W12p3nU7UkjUvaiUrxi8q\n8q5kFa2CJWsys5fdve22AGb2vLtvksWY0qYKVrU88wz88z/DmWfCZz6T92hEpFlaFayka7AOBf4C\nfA3YE9gEWEzwm87CGjLuheghq6fWY0Eh1mRltqlFrbPT+o6n5ftcI/n7VqdwlcQ0ZLQea8HLoyOH\nrAWMbjtdUAprPTOb0cF563R9JCJtvPRSMC3w2GMVrkSqLNEUQXf/YzjF40Z3P8bdDyG4SWXhaWfB\nTtRS6CPj63QjlEWeglhLfwxRFHmqoHYWlPT9J/DHDh7n5TVAEYC//x3Gjw+mBJ55Zt6jEZFuSmsX\nwZFmNomgcvW2lPrsulJUsnJXI5dKVlGqV+3G0W+1sEay961O5kEtzq6CdxL9FwiqZEmK3P3svMcg\n0s6KFfCpT8Hw4TB1Kpgm/4pUWscVrPDmkS25+38DvweOAx5NYVyRxVl3EVuWm17kXsWC7n7Q72bf\nGSni/bniThFNsvZPRERaOv10eO45mDED1l4779GISLdFmSJ46kAvuvssdz/B3W9LOKbY4oSsTHcW\nLO1UQSh3EKrHa1aInQkT6Mq9wlrQVEERkX5deinccEOwHfu66+Y9GhHJQpSA9TEz27K/F81sVArj\nSSzTkBVHHiGrsGqtDycJNlmFikzU87ms1mMpZIlIKmbPhrPOghtvhE1KuYeliMQRJWB9Gjio1Qtm\nthbw9VRGtGbf+5nZQ2b2SHgDzK4oxaYXcVWhilX0gNnve1zLcBAtZDlVUCFLKs7MPm5mD5jZiuZp\n82a2lZm9Ymb3ho+Lm17bzczuD3+GTW06PtjMZprZIjO7a6BfYEo5PfBAsO7qJz+B7bbLezQikqWO\nA5a7Xwv82MwOaxwzsyFm9gWCHZo+kvbgwuB2EbAvsAswwcx2bNcu7nqsTENWHL0+VTDLkFXEdVWy\nUlYhS8olDC3HmdnFZjaj+ZHSJRYQ/Kz7dYvXHnX33cLHxKbjlwDHuvsoYJSZ7RsePxZ43t23B6YC\n56c0RimApUuDGwlfcIFuJCzSiyJt0+7uzwNPmNk+ZnYO8ATB2qypwJVdGN9YYJG7P+7urwMzgfGd\nNCz8phc9P1WwlvcAMlRL0Lae/PIVrGLFpZBVedOBU4BlrLlNe2Lu/rC7LwJa7QG3xjEzGwEMdfd5\n4aEZwCHh8/HheAGuBfZKY4ySv1degYMPhiOPDCpYItJ7ouwieCKAu/8f8EGC6YJfALZz92/TZhOM\nmDYnCHENT4bHOlLZTS/iqkIVqwj6C7kDvr+19MeRhQKHrDhVLKm8/YB/cvfT3f3s5kcG1946nB74\nKzN7X3hsc4KfWw3NP8NW/nxz9xXAi2amVTol98YbQbDabrtg7ZWI9KYoFayDzWyb8Pl/AJe6+4/D\nHwy4+3Opjy4Fldz0ohBTBZOo9f9S6uOrR2/SyRjKVknMevOPAocsVbEq7U9Aon3azOzWcM1U47Eg\n/LPlGuTQ08CW7r4b8CXgKjPbIOqlYw9aCuPf/x2eeQYuu0z3uhLpZVFuNPx+4FEzWwzcCvzRzD7q\n7j+DYDMKd7855fE9BTQv/B0ZHlvDk5OvWPl8w9q72bC268qvR2+4IPKHqkxvQhzHHuR8z6Iaue1y\n159K7SDYrE4qFbBli+MF2Dg3II4r55sQv1yfz8v1+6INQHJlZh9q+nIGcJ2ZfRtY2nyeu9/eSX/u\nvnfUMYRT2F8In99rZn8ERhH8vNqi6dTmn2GN1542s7WBDcNp+C1Nnjx55fNarUatVos6TOmyyy8P\nNrSYMwfWWy/v0YhIJ+r1OvV6PfV+zd07O9FsMnAxsDfwYYL54psD84HbgJ3d/eBUBxf80Hk4vNYz\nwN3ABHdf2Oc8H+ut1hyvLs5vrmNNQ4oTsuL8xj9JwEoljNQTtK31/1KrEND4gD/Q99zye6pHH0e7\nEDLQWAZ8X9uNZSC1BG2bJKkQxglZcSq0MTeOiVN5blfhvts+iLsn+j20mfmxflHkdpfZiYmv3QvM\n7LEOTnN3f3uK1/wVcJq73xN+vSnBhhVvmNnbCTbBGO3uL5rZHOAkYB5wIzDN3W82s4nAO9x9Yrh5\n1CHuflg/1/NOf1ZLPn71KzjsMLjjDthhh7xHIyJxmVkqP3ujTBGc6u7PuvuV7n60u28J7Az8ENiR\nIHSlKpx+eCIwG/gDMLNvuOq2zHYWzHqqYCpq2V1qLjlX7PJWT6ebik4VjEvTBcvP3bfp4JFKuDKz\nQ8zsCYJ/5X9hZjeFL30AuN/M7gWuAY539xfD104ALgMeIdi0qTHT4zJgUzNbRLAxxxlpjFGy9/jj\nMGECXHWVwpWIBDquYLXtyOzr7n5mKp1Fv3ZHFSzIsIoFxa9kFbWKFbfKkmUFq7/3vN/3tN04OlVL\n3kXWVSzIrJIVd/1kf5UsVbDKxcyuc/c1dpo1s5+5+0fzGFMaVMEqrldfhfe9Dw4/HL70pbxHIyJJ\n5VHBauenKfbVNYXf9CJLhdjwooU4wS+tykxR35M0JXmvsqwiamdBiW7Pfo7XshyE9AZ3+PznYdQo\nOLUb+yiLSGm1DVhmtpmZDWl3XmMuehlkFrJ6YqpgLUHbekpjKIhMpt/V0+mmDFMFQTsLSkfM7Jzw\n3oyDG8+bHj8CHs97jFI9F18M994LP/iBdgwUkdV1UsHaEDjbzP7LzD7Q7QFlRSErlHvFpp7jtWvx\nmkWu4tTjXaeo4lax4oasGBSyes4W4WOtpudbEOza9wTwifyGJlX0m9/AOefAz38O66+f92hEpGja\nBix3f9Td/w04E3irmV1sZl8xs627PbhuU8gqsCgVlspuz96fejrd5DFVMMNNLxSyeke48dLRwAmN\n5+HjGHef5O6P5j1GqY6nn4ZPfhKmT4dtt817NCJSRB2vwXL319z9anefCFwOfMLMLjGzo8ws99/f\njCZ6WJJQ4ipWLWH7esL2RVYvdr8VXo8Vl0JWebn7f5vZ9mb272b23fDP7fMel1THP/4BH/84TJwI\n++2X92hEpKhibXLh7k+7+zfd/fPAQoIphFPNrL8FxpmIE7JUxSqwnqtM9YiCr8cChayyMrPDCe7N\n+E7gb8Bo4N7wuEhiJ58Mw4fDpEl5j0REiizxLoLuPtfdTwNOBzYzs0vNLJft2kEhC4gXsspYxVq2\nuDghLNYW8UnV07lGWaYKgkKWtPM14AB3/6S7/1t4494DgK/nPC6pgMsvh3o9mBq4Vpp7MItI5aT2\nT0TTFMLjge+m1W8cClllVV/zUH8f/jsKBS3664bcQ149eRdlClkxVGn7djPbz8weMrNHzOz0fs6Z\nZmaLzOw+M3t3u7ZmNszMZpvZw2Z2i5lt1PTapLCvhWa2T9Px3czs/rCvqU3Hjw+PzzezO8xsx/D4\nu8zst2a2IBzXoWm/N8BQ4K4+x+YAuU9jl3KbNw/OOCPY1GLDDfMejYgUXccBy8zOMrOamQ3qc3xd\nM9u9+Zi7v5TWAOOqXMiKqpRVrH40f/gvUtVqQPW8BxBdWUJWhpteFI2ZrQVcBOwL7AJMaASYpnP2\nB7Z19+2B44HvddD2DOA2d98BuB2YFLbZGTgU2AnYH7jYbOWG1JcAx7r7KGCUme0bHr/S3d/p7rsC\n3wQuDI+/Anza3UeHfU01s7Q/ql4AfN3M1gvHPwT4z/C4SCzPPResu/r+92HHHdufLyISpYI1HpgM\nLDWz683sRDPb3t1fAwaZ2cSujDCBQoesqEozVTCpeuvDkYNVP/20E/U6hQl79XS6Kcz300bvhqyx\nwCJ3f9zdXwdmEvzb3Gw8MAOCKdzARmY2vE3b8cD08Pl04JDw+cHATHdf7u6LgUXAWDMbAQx193nh\neTMabdz9r01j2QB4Izy+yN3/GD5/BngWeEuSN6OFicApwMtmthR4Cfgi8Hkz+1PjkfI1pcLc4eij\ng10DDzmk/fkiIgCD2p+y0pnufrOZbQB8CNgHONnM1ib4jee6wMVdGGM1jSP6h8T3k+m0qvzU6Vo1\nrFONoDFQ4Ow3jNRTHUrn6uT6vs0lXqi/k3i/QJhDNtXgYtmc4L5ODU8SBKd252zepu1wd18K4O5L\nzGyzpr6ap9w9FR5bHrbvew0Awl+4nQqsQ/DzYjVmNhZYpxG4UvSplPuTHnfRRbB0Kfz0p3mPRETK\nJMo27TeHf/7V3a939xPDKSh7Ab8hmApSOIWuYmWxHivzKlYtQdtm9Zza9tGoaJWlstNr67Ey3PSi\nxKz9KWvwJBd094vdfTuCzY++utpgzN5KUPE6Ksk1+rnurzt5pH1dqabf/z64mfCPfwyDB+c9GhEp\nkygVrJbc/THgsRTG0jWjWcACou0INnrDBZF3ERsy7oXoH97iVLKi2oNs71lUVaUKWbVkXSxbHD9o\nl6CS9eqcYdlM7Q118u/Py/X5vFy/r91pTwFbNn09MjzW95wtWpwzeIC2S8xsuLsvDaf/Pdumr/6O\n93U14RowADMbCvwCmNQ0vTA1ZrYu8B/ABODN7r5RuDHHKHe/KO3rSXX97W9w2GEwdapuJiwi0fXM\nRqOVqmRlsatgaatYcdqkKe/rN9STd1GmSlYMRatkbVjblZGTj1756Mc8YDsz28rMBgOHAdf3Oed6\n4AgAMxsHvBhO/xuo7fWsqigdCVzXdPwwMxtsZtsA2wF3u/sS4CUzGxtuenFEo42Zbdc0ln8BHgmP\nrwP8LzDd3X8e5b2J4ELgHcC/sqoK9wfg8126nlTUySfDHnvAv/5r3iMRkTLqmYAFBQ9ZUWmqYMJz\nuyHv6/dVT95FWap2PbLphbuvAE4EZhMEh5nuvjDcGv248JxZwGNm9ihwKcHGD/22DbueAuxtZg8T\nTPs+L2zzIHAN8CAwC5jo7o3gcgJwGUGAWtSYRg6caGYPmNm9BBtOHBkePxR4H3BUuIX7vWb2zpTf\noo8Ah7v7XazaXKOxbkykI1dfDXfcAd/5Tt4jEZGyslU/K8vLzPzYCLM/ok4XhHg3HY384S3Oh8So\nv/GPU1lI9CG7nqBtQ63D/msxrjdQ31FEvW7Wasmaxw3bcYI9xK/Sxtz0or9firy60Sa4e5w1TCuZ\nmY+Nseznbvtg4mv3GjN7HHinu79kZs+7+yZm9hZgjruXdqKXmXkVflaXwWOPBZWrm26CMWPyHo2I\nZEd8/RgAACAASURBVM3MUvnZ21MVrIasKlmRFfX+WInUUuij3s+xVsfT6DuPPrqtnqx53JBdgk0v\noHyVLOnXT4Dp4XTGxoYaFxFsSS8yoNdfh8MPD24orHAlIkn0ZMDKSmHXY0UNWYW6AXGdgcNCnGsN\n1F8322atns9lSxKypBLOJNh0aQGwMcF9u54Gzs5zUFIOZ58NG28Mp5yS90hEpOx6NmAVej1W5Ta9\ngOQhq076wapv/1m0yVudRDdhjqsEIUtVrPJz93+4+xfdfQNgOMHNkL/o7v/Ie2xSbLffDpdfDj/8\nIazVs5+MRCQtlflnZFfmR25T6JAVVeE3vYAgBNUS9tFN9Q7PaTzKrB6vmUKWFJiZ7Rxu+DEJ+Ciw\nU95jkuJ77jk44oggXA0fnvdoRKQKKhOwoGIhq5I3IW6opdBHt9TbPKqkHq9ZmUJWDApZ5WOBywmm\nBp4JHAz8O3C/mV0RbiUvsgZ3OOaYYDv2ffbJezQiUhWVCligkBVZJUJWmn31mjqxgpa2b5diOY7g\nH4Jx7r6Vu7/X3bcE3kvwq6fj8xycFNcPfwhPPgnnnpv3SESkSioXsKDYISuyIm56ASmGrFoKfXRy\nTAZWj95EOwtKcXwaOMnd5zUfDL8+JXxdZDVPPQWnnw5XXAGDB+c9GhGpksrcB+siP3aN4/PZNXJf\nWdwjK9YHt6gfEuN8GM38Hlnt1Ps5XkuhD2mtFr1Jle+RtW/y+2HoPljdZ2bPA1u5+7IWrw0F/uTu\npU3Mug9W+tzhoIPgPe+Bs87KezQiUhS6D1YHsqpkRVXYnQVzq2T1p9bPI04f0pl69CYVr2RJKazd\nKlwBhMcr/bNOovuf/4EnnoBJk/IeiYhUUeV/6MQJWVFVavv2uCGrq0ErDTUUtDpVj95EIUvytY6Z\n7WlmH2r1AAblPUApjmeegdNOC9ZfaWqgiHRDpacINhR1qiBUbLoglGTzg3reAyiRWrTTqzZdUFME\nS8HMFgMD/jBz922yGU36NEUwPe5wyCHwzndqYwsRWZOmCEZQ5E0vCnmPLIj/gbc01SzpTD3a6apk\nSQ7cfWt332agR95jlGL48Y/hj3+Er3wl75GISJX1RAWroVKVrDgfELOsZDUUuqJVz3sAJVKL3iRO\n0M6yktVJFSulCtaQl56P3O7VjTZRBUsAVbDSsmQJvOtdcOONsPvueY9GRIpIFawYKlXJyuJGxBD/\nA29DKSpa0l49epM44TrLSpaqWCI9wx0mTgxuKqxwJSLd1lMBC4odsiIrS8iCVUGrUGGrlvcASqYe\nvYlClogUwDXXwEMPaUt2EclGzwUsKG7IymRnQYgfstIIWlCwsFXLewAlUyeTdVlJp6ZGoZAlUmnP\nPgsnnxzcUHi99fIejYj0gp5ag9VXUddkZbKzIMTfICDLD7/Q+gN6f+Es9pqvesx2vawW7fSs1mSl\nubOg1mBJAWgNVjKHHgpbbw3nn5/3SESk6LQGq484FaaevhExxP8gmmY1qxPNFa92la/CVMZ6QT3a\n6VlVsrSzoIiErr0Wfv97OPvsvEciIr2kMgELihuyCnsjYogfsiDbkBVV5JBV68IgekE92ukKWSKS\nkb/8Bb7whWBq4JAheY9GRHpJpQIWZBeyoip8yCpLNSsKhayM1KOdrpAlIhmYNAk+/nH4p3/KeyQi\n0msqF7CyUtjt2yFeyILk1awiBq3IUwZr3RlH5dWjna6QJSJdNHcu/OIXcO65eY9ERHpRJQNWUacK\nQsVDFqwKWkULW1qXlYF6tNMVskSkC1asCO55NWUKbLxx3qMRkV5UyYAFCllAfiGroWhBq+NqVg1V\nsuKqRzu96CGrgMxsPzN7yMweMbPT+zlnmpktMrP7zOzd7dqa2TAzm21mD5vZLWa2UdNrk8K+FprZ\nPk3HdzOz+8O+pjYd/6KZ/SG89q1mtkWfsQ01syfMbFpa70lWzOz88H24z8x+amYbNr0W9X0abGYz\nwzZ3mdmWWX8/VfW978EGG8CnPpX3SESkV1U2YIFCFpAsZKUdtIoUtjpSy3sAJVWPdrpCVsfMbC3g\nImBfYBdggpnt2Oec/YFt3X174Hjgex20PQO4zd13AG4HJoVtdgYOBXYC9gcuNrPG9rWXAMe6+yhg\nlJntGx6/Fxjj7u8Gfgp8s8+3cS7w66TvRU5mA7uE39sikr1PxwLPh/+dpgLaRDwFS5fC5Mnw3e+C\n6SYHIpKTSgcsUMgCgpCVdzWrYQ/yD1yR1mXVujeOSqsTKWgtWxw9aPVmyBoLLHL3x939dWAmML7P\nOeOBGQDuPhfYyMyGt2k7HpgePp8OHBI+PxiY6e7L3X0xQagYa2YjgKHuPi88b0ajjbv/2t3/Hh6f\nA2zeGJiZjQE2IwgqpePut7n7G+GXc4CR4fPI7xOrv+fXAnt1e/y94N/+DY48Et7xjrxHIiK9rPIB\nCxSyVipCNauvPMOWQlYG6tFOV8hqZ3Pgiaavn6QpwLQ5Z6C2w919KYC7LyEIQa36eqqpryfbjAOC\nKs1NAGFF51vAaUAVagvHALPC53Hep5Vt3H0F8KKZbdLNAVfdnXfCL38JZ52V90hEpNf1RMAChayV\n4oYs6G7QgjWrW1mErkghq9a1YVRbPdrpCllpixNmPPFFzT4FjGHVFMGJwI3u/nSCcXVduG7s/qbH\ngvDPg5rO+XfgdXf/cZqXTrGvnvP668HGFhdcAEOH5j0aEel1g/IeQJZGs4AFjI7UZlfmM59du36d\n0RsuYMHL0doMGfcCr84ZFqkNEISsJDukvZ/sPqD2DVlxPky3M3TrCB/qa0QODELwntU6P33Z4mg7\nP84leiC/k+7+wqBJR/+f/r4O99fbnfUU0LwZwsjwWN9ztmhxzuAB2i4xs+HuvjSc1vZsm776Ow6A\nmX2YYH3SB8LpiADvBd5nZhOBocA6ZrbM3c9s901nyd33Huh1MzsKOAD4UNPhOO9T47WnzWxtYEN3\nf76/606ePHnl81qtRq1WG/gb6THf+Q6MGAGf+ETeIxGRMqnX69Tr9dT7NffEv6jMnZn9//buPlqu\nur73+PtDEEElmEgNrYggGDSQ8lRDLIKnIBJgNcH2VmPtAgQUBS5xgVYQLyCiAgoCVbBeuQoIcvEJ\noeW5OLbYBkIACRAgqImAPJWnSxFpCN/7x+xJdk7OnDOzZ8/sh/m81pqVmX32b+/f7JycOZ98f/v3\ni5/HrI737zb8AF2HrKzn6TZkQYe/vI0lj2moi6wE5B22uqqcNHI++bAY6W73bqfXz1L1HC9knSQi\noqfKgqTgugw/Z/dZ99zJL+L307xf51HgVuBDEbE0tc9+wJERsb+k2cDZETF7vLaSTqc54cLpyeyC\nUyLiuGTyhktoXtk3ATcAb4uIkLQQOBpYBPwzcG5EXCtpJ+AHwD4R8as21+QgmhNhHN39hSmOpDnA\nmTSD41Op7Vmu0xHA9hFxhKT5wAERMb/NeaMOn9X98sgjsMMO8O//DtOnF90bM6syqffPfRiiIYJp\ndRwuOPDJL1r6PWxwPHkPJex6QeKRnE48TBrd7e7hgmtJ7tU5iuYkEffQnFhhqaTDJX0s2edq4DeS\nHgT+kebQvLZtk0OfDuwtqRXATkva3AtcDtxL836jI1K/6R8JXAA8QHPyjGuT7WcArwV+IOkOSVf0\n52oU4h+A1wE3SLpd0nmQ+TpdAGwqaRnwSZozOVoGxx4LH/+4w5WZlcdQVrBa6lbJgoKrWVCOX1Z7\nrWx1PW14o8cTDqOR7nYvqpJVsgqWDSdXsNr7l3+Bww6De+6B17ym6N6YWdW5gjVKpmpRzSpZ0OPk\nF71Ws2BNRauoqhb0XtHq9pd5V7MyaHS3uytZZjbKSy/BkUfCOec4XJlZudQmYIFDVkvmkAX5hKyW\nMgwfzKrrkAUOWd1qdLe7Q5aZpZx1FrztbTB3btE9MTNbW60CFjhktZQmZEHxQSurzCFrpIeTDptG\nd7s7ZJkZsGIFnHkmnHtu0T0xM1tXaQOWpDMkLZV0p6QfSZrcaVuHrKaeQ1a/gtagw1Yv1ayNt3TQ\n6rtGd7s7ZJkNvWOOgQULYKutiu6Jmdm6ShuwaM50tV1E7Agso7mmSl/VNWSVqprVUsWglckIDlqd\naHS3u0OW2dD6+c9h8WL49KeL7omZ2dhKG7Ai4saIeCV5uZDmAo0dyxJisrYbZMiqTTWrpYiq1sBD\nFjhodaLR3e6DCFlmViqvvNKsXp1+Omy4YdG9MTMbW2kD1iiHANd026iOIQsKGjII/Q1aMNiwVUjI\nAgetiTS62/355d0FLYcss0q76CJ49avhAx8ouidmZu0Vug6WpBuAaelNQAAnRMRVyT4nADtHxF+P\nc5x4OjZqe55M61BlaAODWycLClgra7S81s4azyCGaWX9pbvr9bLG0sjhGHU00n2TbsJvpwH7Rq+D\nZcXzOlhNL7zQXEz4Rz+C2f38zz4zG1p5rYNV6oWGJR0MfBTYMyJeGme/+PuT1l/9+t0j6/HukUlr\n7eOQta5cgtYgQlZLP8NWoSELHLTGMtJ9k15D1tMNeKax5vVvPu+AZYVzwGo66SRYtgwuvbTonphZ\nXdU+YEmaA5wJ7BERT02w77gVrBaHrHVVqprV0q+g1cvwMQetPhnpvkmelSxXsKwEHLDg4Ydhhx3g\njjtgiy2K7o2Z1dUwBKxlwAZAK1wtjIgj2uzbUcACh6x2HLRSCq9mgYNW2kj3TfIKWQ5YVgIOWHDg\ngc1gdeqpRffEzOqs9gGrG90ELKhvyIKSVLNgsEEL8g9bpahmgYNWy0j3TfIIWQ5YVgLDHrBuuw3m\nzoX774eNNy66N2ZWZw5YKd0GLHDIaifXkAUOWrlp5HisqhrpvkmvISuvgPX5DD9nT3LAsqZhDlgR\nsMcecPDBcOihRffGzOour4BVlWnac5dpHaqM06pnncJ90NO4Qw4LE4/W76ndR8t7ives07lDDlO6\np43gKd4z8BTuZpX24x/D8883A5aZWVXUJmBNWfxi123KHrJ6OV8vIQtyWDNrtEEGrbzX0tqV3tbN\nyjVowfAGrUa2Zg5ZZpX00kvw938PZ54JkyZNvL+ZWVnUJmDBYEPWoBYjbp0vU7scQpaDVkqv1SwH\nrRw0sjVzyDKrnH/4B9huO9hrr6J7YmbWndrcgxW3rXn9zC7d3Y8FPczel6Fdlnuysp5rddseZxmE\nPtyf1TKo+7Tyuj8rj1/Ac70/q6XRh2OW1Ui2Zt2G3Od9D5YVbxjvwXrySZgxA37xi+biwmZmg+B7\nsMYxqEpW1naDrmRB79Us6MOwwZZBVbXyqmb1MmSwxRWtHjWyNetLsDWzvJ18Mnz4ww5XZlZNtaxg\ntbiSNUbbHCpZ0MdqFgymopXnbIOuaBVoJFuzTsOtK1hWAsNWwbr3XhgZgfvug6lTi+6NmQ0TV7A6\n4ErWGG0nLyl3NQsGU9HK8/6sXqtZ4IpWZo1szVzJMiutT30KTjjB4crMqqvWAQvqHbLKMGSwFkEr\nD3kMG4Q+B62RnI9rZpav666DBx+ET3yi6J6YmWVX6yGCaWUfLgjFDBmEigwbhP4PHSzTJBgtfa+0\nNPp8/EEZydZsojDrIYJWAsMyRHDVKthhB/jiF2HevKJ7Y2bDyEMEu1T2ShYUM2QQ8qlmQQ0qWmWr\nZkGfKlppIwx1dauCQwUlzZF0n6QHJH2mzT7nSlom6U5JO07UVtIUSddLul/SdZI2SX3t+ORYSyW9\nL7V9Z0l3Jcc6O7V9d0mLJa2U9Fej+vXm5Pj3Srpb0hZ5XRervgsvhDe8AebOLbonZma9GZqABQ5Z\n47bPKWRBn+/Pgv4GrbzvzapM0IJqB61G9qYVClmS1gO+DuwDbAd8SNLbR+2zL7B1RLwNOBz4Zgdt\njwNujIhtgZuA45M2M4APAO8A9gXOk9T6n73zgUMjYjowXdI+yfYVwEHAJWO8hYuA0yNiBjALeKKH\ny2E18vvfw4knwle+AnLd1swqbqgCFtQ/ZJVhAgwYQDULHLT6ZoRqBq1G9qbVCVmzgGURsSIiVgKX\nAaMHU82jGWSIiFuATSRNm6DtPODC5PmFwAHJ87nAZRHxckQsB5YBsyRtBmwcEYuS/S5qtYmI30bE\n3cBaY9okvQOYFBE3Jfv9PiL+0NvlsLo4+2zYbTeYNavonpiZ9W7oAhbUO2T1cs7V7XOuZlU+aOXF\nQavcqhGy3gQ8lHr9cLKtk33GazstIh4HiIjHgDe2OdYjqWM9PEE/RpsOPCfpR8kQwtNT1TAbYk8+\nCWedBV/6UtE9MTPLx1AGLHDImrB9jtUsqHjQyrOaBfmFLHDQWkejt+bVCFndyhJi+jGjwvrAu4Fj\ngHcCWwMH9+E8VjFf+AL87d/C1lsX3RMzs3ysX3QHijRl8Ytdzy44kyWZZu3L2m4n7sg8u2ArZPUy\ny+DMyUtym2UQ1tyf1dcZB2fTnxkHWyErj9kGWyErrxkHWyGrrwFhJPW80cfz9KpBT4Hw+eX5h9ZO\nvmeebsAzjYn2egRITwyxebJt9D5vHmOfDcZp+5ikaRHxeDL8r3VvVLtjtds+noeBOyNiBYCkK2j+\nS/jOBO2sxh58EC69FJYuLbonZmb5qU8Fa4Jp2tvJWsnKUiHqpZJVp2oWVHzGwbIOG4Q1Fa2BVbVa\nj5opopI1dQS2PnnNY2yLgG0kvUXSBsB84MpR+1wJHAggaTbwbDL8b7y2V7KmmnQQ8NPU9vmSNpC0\nFbANcGsyjPA5SbOSYX4Hptqkpatni4DXS3pD8npP4N52b9SGwwknwDHHwB/9UdE9MTPLT33Wwfpm\nasOfdX+MLOtkQbbqUC8VpazVrF7Pu/oYOVaz0iq7hlZea2e15LmGVkshw94aBZwT8g97W+WzDtZ7\nM/ycvXHstTgkzQHOofkfZBdExGmSDgciIr6V7PN1YA7wAvCRiLi9Xdtk+1TgcppVqRXAByLi2eRr\nxwOHAiuBBRFxfbJ9F+C7wIbA1RGxINn+Z8BPgNcDfwAei4iZydf2As5K3spi4GMR8XL3F2e41HUd\nrFtugb/+a3jgAXjNa4rujZlZfutg1TNgQelDVi/teglZvZx3rWP0IWj1PWSBg1YhGgM6z0gfjlm+\ngGXDp44BKwJGRuCgg+CQQ4rujZlZkwNWypgBCxyy+nDedY7joLVG3iELaha0Who5H28k5+OlOWBZ\n8eoYsK66Co4/Hn75S5g0qejemJk1OWCltA1YUOuQBcUPGQQPG1xHFapZLYWHLegtcI3k1Id2HLCs\neHULWC+/DH/6p81Fhfffv+jemJmt4YCVMm7AAoesPp57reM4aK3NQSujRgf7jPS5Dy0OWFa8ugWs\nb38bLrkEbroJvBKamZWJA1bKhAELHLL6eO51juWgtUZVhg22lCpolYEDlhWvTgHrhRdg+nS44gp4\n5zuL7o2Z2docsFI6CljgkNXn8691nD6FLHDQWovDVp85YFnx6hSwTj0V7rkHvv/9ontiZrYuB6yU\njgMWVCJk9dp2GKpZ0OegVZVhg9DfkAVDHrQcsKx4dQlYTzwBM2bArbfCW99adG/MzNaVV8Cqz0LD\nncqwIHGWxYihtwV+e2nby6LEvZ57nWP1YZHilkouVLw7+S5UDPkvVjzaQBYuNis/SWdIWirpTkk/\nkjQ52f4WSb+XdHvyOC/VZmdJd0l6QNLZqe0bSLpM0jJJ/yFpiyLe0yCdcgr83d85XJlZ/Q1fBasl\nQyULPGSwp2NVtZoFHjY42tBUtVzBsjUkvRe4KSJekXQazcWdj5f0FuCqiPjTMdrcAhwVEYskXQ2c\nExHXSfoEMDMijpD0QeD9ETG/zXkrX8Fatgze9S647z7YdNOie2NmNjZXsHqVoZIFg69m9VrJGqZq\nVt8rWnnrRzUL+l/RAle1bChFxI0R8UryciGweerL63wgS9oM2DgiFiWbLgIOSJ7PAy5Mnv8Q2Cv/\nHpfHZz8Ln/qUw5WZDYfhDVgwFCEL8hky6KBFtYYNwmCDlsOWDZ9DgGtSr7dMhgf+TNK7k21vAh5O\n7fNwsq31tYcAImIV8KykqX3ucyEWLmw+FiwouidmZoNRn4CVdQhXhUJWkfdltfqQp36FLPD9WWsZ\nRNACBy2rBUk3JPdMtR5Lkj//MrXPCcDKiLg02fQ7YIuI2Bk4FrhU0uu6PXU+76BcIpqVq1NOgY2y\njbA3M6uc9YvuQK4Wku0X39vIdE/WlMUvZronayZLMt/b1EvbVsjq5d6sVsjK696sVsjqx/1ZrZDV\nt/uzZtOfe7NaISvve7RaIavf92ilQ9bQ3KtldRERe4/3dUkHA/sBe6barASeSZ7fLulXwHTgEeDN\nqeabJ9tIfe13kiYBkyPi6XbnPfnkk1c/HxkZYWRkpNO3VKgf/KC59tWBBxbdEzOzdTUaDRqNRu7H\nrc8kFwenNmStLlRk4ote20L5JsBYfcyqToRRpWndWwYxGUZLZYNWTpNcbJzh5+zznuSibCTNAc4E\n9oiIp1LbNwWeTia/eCvwc5oTWDwraSFwNLAI+Gfg3Ii4VtIRwPbJJBfzgQPqNsnFH/4Ab387fPe7\nUJE8aGZDzutgpawTsMAhqwN5hKw8+jHmMR201tbPoAUOW205YNkakpYBGwCtcLUwCUh/BZwC/Dfw\nCnBiRFydtNkF+C6wIXB1RCxItr8auBjYKTne/IhY3ua8lQxYX/4yLFoEP/5x0T0xM+uMA1bKmAEL\nKhOywEFrzONVNWSBg1anSh+2HLCseFUMWI89BttvD7fcAltvXXRvzMw644CV0jZggUNWh8oasqDC\nQatfIQvqM3SwpbRBywHLilfFgHXYYTBlCnzlK0X3xMyscw5YKeMGLBh4yILhHjIIDlprcTWrO6UK\nWw5YVryqBaw77oB994X774dNNim6N2ZmnXPASpkwYLVkCVpDFrJgeKtZ4KA1pqKCFpQgbDlgWfGq\nFLAi4C/+AubPh49/vOjemJl1xwErpeOABZUZMthrOHE1K7tKhixw0OoLBywrXpUC1k9+Aiee2Kxi\nrV+vhWDMbAg4YKV0FbCgMiELXM2a8LgOWuuqc9CCAYctBywrXlUC1ksvwXbbwfnnw97jriZmZlZO\nDlgpXQcscMjqwjBXs8BBa1xFhy3oc+BywLLiVSVgffWr8POfw1VXFd0TM7NsHLBSMgUsGJqQlUd7\nGO6g5ZA1gTIELehD2HLAsuJVIWA9+STMmAE33wzbblt0b8zMsskrYK2XR2cqK+svtrdlazZl8YsZ\nTwgzWZK5bR7tAXbijp6P0ZJHf9Y55uQlzJyc/3EBNpr9DBvNfqYvx2Y22cP+RHZPHv22a/Io2sZb\nrnnUlKQ5ku6T9ICkz7TZ51xJyyTdKWnHidpKmiLpekn3S7pO0iaprx2fHGuppPeltu8s6a7kWGen\ntm8g6bKkzX9I2iL1tdMl3S3pnnQbq74TT4QPf9jhyswM6lTBmgnskvEAFZrGHYofMgjlr2aBK1pj\nGraKVlqm6la5KliS1gMeAPYCfgcsAuZHxH2pffYFjoqI/SXtCpwTEbPHayvpdOCpiDgjCV5TIuI4\nSTOAS4B3ApsDNwJvi4iQdEtynkWSrk7Oc52kTwAzI+IISR8E3h8R8yW9CzgjInaXJOAXwHER8a/d\nX5zhUvYK1t13w157wdKlMHVq0b0xM8vOFayxLM7YbsCVLCiumjWTJUNRzQL6Vs0C+lfNgv5Vs2D4\nKlpp6epWdStcs4BlEbEiIlYClwHzRu0zD7gIICJuATaRNG2CtvOAC5PnFwIHJM/nApdFxMsRsRxY\nBsyStBmwcUQsSva7KNUmfawfAnsmzwPYUNKGwEbA+sDjma+ElUIEHHMMfO5zDldmZi31CljgkDWg\n9pB/yPKwwZR+DhuEwYQsKF/IShsduKoRvt4EPJR6/XCyrZN9xms7LSIeB4iIx4A3tjnWI6ljPdzm\nWKvbRMQq4DlJUyNiIdAAHk2Oc11E3D/hO7ZSu/pqeOghr3llZpZWv4AFxYSsIb4vy0HL1axxlbGa\nNZHyB61uZBnqkOd4NAFI2hp4O/AnNEPYXpJ2y/E8NmArV8Kxx8KZZ8KrXlV0b8zMyqO+ywAuJts9\nWQvJ/kvtbWS6L2vK4hcz35PVCiNZ72PqtX3LTtyR631ZM1nSl3uzZk5e0pd7s1ohqy/3ZrW+H/t1\nb1YrZPX7/qxWyCrj/Vn91tH9Xwvp4C/5EWCL1OvNk22j93nzGPtsME7bxyRNi4jHk+F/T0xwrHbb\n021+J2kSMDkinpZ0CLAwIl4EkHQN8C6a92JZBZ1/Pmy5Jey7b9E9MTMrl3pWsFoGXcmCnipZrmat\nrarVLA8bnEAVK1oDMRv4ZOoxpkXANpLeImkDYD5w5ah9rgQOBJA0G3g2Gf43XtsrgYOT5wcBP01t\nn5/MDLgVsA1wazKM8DlJs5IJKw4c1eag5PnfADclz38LvEfSJEmvAt4DLO3o0ljpPPUUnHpqs3ol\nLyZgZraWes0i2E7W2QVhqGYYzPMYkO9Mg1C92QYrO9MgDG62QSh3RSuHtagkBfwmQ8uxZzCUNAc4\nh+Z/kF0QEadJOhyIiPhWss/XgTnAC8BHIuL2dm2T7VOBy2lWnlYAH4iIZ5OvHQ8cCqwEFkTE9cn2\nXYDvAhsCV0fEgmT7q4GLgZ2Ap2jOVLg8mcXwPGAP4BXgmoj4dIYLM3TKOIvg0UfDqlXwjW8U3RMz\ns/x4oeGUCQNWi6dxH+gxwCELHLQ6VsagVcKAZcOnbAFr6VLYY4/mn5tuWnRvzMzyMzQBS9KxwFeA\nTSPi6Tb7dBawYGhCFvQeRvIMMw5aDlodK1PQcsCyEihbwNp//+a6V8ccU3RPzMzyNRTrYEnaHNib\n5pCVfAzJNO6Qz31Zed0Dlee9WeC1s9bRz3uzYHD3Z4Hv0TIrsZ/9DB54AI46quiemJmVV6krWJJ+\nAJxC86bpXXKpYLUMupIFHjKYcDXL1ayuFFnRcgXLSqBMFaz994f3vx8OO6zonpiZ5a/2QwQlUhdG\n1wAAD8lJREFUzQVGIuIYSb8h74AFQxWyoFxBK++QBdULWg5ZXSoiaDlgWQmUJWDdfz/svjusWAEb\n9fZxZGZWSrUIWJJuAKalN9Fc4PJzwGeBvSPi+SRg/VlEPNXmONkCFjhkFXSMFlezHLS6Nsig5YBl\nJVCWgHXEEfCGN8AXvlB0T8zM+qMWAasdSdsDNwK/pxm6WotYzoqIJ8bYPxYAr39j8/XIa2HkdV2c\n0CGrkGO0uJpV8ZAF9QlaLzdgVWPN6//+vAOWFa4MAevpp2HrreHee+GP/7jQrpiZ9U2tA9ZoSQVr\n54gYc5YASdH69WXLQVeyoJAZBsFBayJVC1lQ8aBVRMiC/la0XMGyEihDwDrttOa07BdeWGg3zMz6\natgC1q9pDhFsew9W+teXSoUscDUrxdWsiocsqFfQcsCyEig6YK1cCVttBVddBTvl/yPazKw0hipg\nTWR0wAKHrG7kFUDKHLSqFrLAQSuzPIOWA5aVQNEB6/vfh3/8R2g0CuuCmdlAOGCljBWwwCGrW65m\n9XBcV7PGVlTIgnyClgOWlUCRASsCdt0VTjgB5s0rpAtmZgPjgJXSLmBBDyELKjf5Bbia1al+BC1X\ns8ZR1WqWA5aVQJEB6xe/gIMOak7RPmlSIV0wMxuYvALWenl0psyWL+mh8eKM7Xr5hfW2HtoCUxa/\n2FP7mfRywfI/DsBO3JHbsVry7N/qY05ewszJ+R8XYKPZY87vko9e/kOgU7sP4Bxj2TV5mFkmZ58N\nRx/tcGVm1o3aV7BaCqlkwdAPGczzOOBqFria1ZNuK1q5VbC+k6HlR1zBMqC4Ctby5bDLLs0/N954\n4Kc3Mxs4DxFM6SRgtQzTfVngIYOd8r1ZKXUPWdB50HLAshIoKmAdeyxI8NWvDvzUZmaFcMBK6SZg\ngUNWFmWbAAOqE7RczRpH2YOWA5aVQBEB6/nnYcstYfHi5p9mZsPA92D1IPN9WVnvyYJK35cF+dyz\nNJMlvjcrZ743q0e+R8tsTN/5Duy5p8OVmVkWQ1nBaqlcJQt8X1Ybrmb1sZo1iEoWlLOa5QqWlcCg\nK1irVsH06XDxxfDnfz6w05qZFc5DBFOyBiyo4DTuUJshg3keB6oTssD3ZrVVdMiCtYOWA5aVwKAD\n1hVXwJe/DAsXNu/BMjMbFh4iOEojY7vCpnH3kMFcjwPNIYN5DxvMe1jj6uN6yODYih4yCB42aEPv\na1+DT37S4crMLKvaVLBa/z88kvEYwzaNO5SrkpX3sVzNcjUrFze6gmXFG2QF6/bbYd48+PWv4VWv\nGsgpzcxKwxWsNhoZ2xVSyYLeK1k9VLOmLH4xl4WJXc3q8biuZo2tDNUssyFz9tlw1FEOV2Zmvahd\nBatlJOOxKlnJAlezxjHs1azKV7KguGqWK1hWAoOqYD36KMyYAb/6FUyd2vfTmZmVjitYE2hkbLd8\nSQWncYfS3JflalaPx+1DNWuj2c/0r5o1G1ezBkzSHEn3SXpA0mfa7HOupGWS7pS040RtJU2RdL2k\n+yVdJ2mT1NeOT461VNL7Utt3lnRXcqyzU9s3kHRZ0uY/JG2R+tpByf73Szowz+syCJJOkfRLSXdI\nulbSZqmv5XadivKNb8CHPuRwZWbWq9oGLMgesgAuKypkFTD5RSNpl8eQQch3Aoy8g9ajjQdyOx70\nb92sPKz6t5vXel3rIYNPNwbQgeJJWg/4OrAPsB3wIUlvH7XPvsDWEfE24HDgmx20PQ64MSK2BW4C\njk/azAA+ALwD2Bc4T1o99cH5wKERMR2YLmmfZPuhwNPJ+c8GzkiONQU4EXgnzWlETkoHuYo4IyJ2\niIidgH8GToJ8r1NRXnwRvvUtWLCgyF50p9FoFN2F0vE1GZuvy9h8Xfqn1gELsoeshfRYyarQfVmN\nUX0tU8jK+1jR+Le+VLPylsfixK/cfPM622oRssYKWs80BnDyUpgFLIuIFRGxErgMmDdqn3nARQAR\ncQuwiaRpE7SdB1yYPL8QOCB5Phe4LCJejojlwDJgVlK52TgiFiX7XZRqkz7WD4E9k+f7ANdHxHMR\n8SxwPTAn+6UYvIj4r9TL1wKvJM/zuE579bPvE/ne92DWLNh22yJ70R3/crguX5Ox+bqMzdelf2of\nsKC3SlYlJ78ADxmcgIcMeshgRb0JeCj1+uFkWyf7jNd2WkQ8DhARjwFvbHOsR1LHerjNsVa3iYhV\nwHOSpo5zrEqRdKqk3wJ/S7MiB/lcp2eT6zRwEc3JLT75ySLObmZWP0MRsMAhK4s8QhaUe8hg3qoS\nsqAm1SzrRJabdfOcUaFSE3VIuiG5Z6r1WJL8+ZcAEfG5iNgCuAT4n3meOsdjdeXll+Ezn4G9Cq2h\nmZnVSERU/kHzlwE//PDDj9wfOfx8Wp7x3I+NcazZwLWp18cBnxm1zzeBD6Ze3wdMG68tsJRmFQtg\nM2DpWMcHrqV5/9TqfZLt84Hz0/skzycBT6T2+Wa7flbtAbwZuCvv6+TPOD/88MOPYh95fEasTw2E\npzI2s5KKiC1zPNwiYBtJbwEepfkL+4dG7XMlcCTwfyXNBp6NiMcl/ec4ba8EDgZOBw4Cfprafomk\nr9Ec0rYNcGtEhKTnJM1K+nQgcG6qzUHALcDf0Jw0A+A64IvJxBbrAXvTDCaVIWmbiHgweXkAzfAK\n+V6ndfgzzsysWmoRsMzMhkFErJJ0FM0JItYDLoiIpZIOb345vhURV0vaT9KDwAvAR8Zrmxz6dOBy\nSYcAK2jOiEdE3CvpcuBeYCVwRCQlFZoh7rvAhsDVEXFtsv0C4GJJy4CnaAY5IuIZSV+gOXg5gM9H\nc7KLKjlN0nSak1usAD4O+V4nMzOrvlosNGxmZmZmZlYGQzPJRS8kHSvplaJmeBoESWckC2TeKelH\nkiYX3ae8dbJAax1I2lzSTZLuSW7QP7roPvWbpPUk3S7pyqL7YpbFeIs9j9rvAkmPS7pr1PaTJD2c\n/Du4XVKlpsAfSw7XpKP2VdPFdWm3sHhtvlc6+VxXlwuv10GG67JTavtyrVlQ/dbB9br/JroukraV\n9O+S/iDpmG7ajuaANQFJm9O8V2BF0X3ps+uB7SJiR5pruBxfcH9ypQ4WaK2Rl4FjImI74F3AkTV+\nry0LaA7PMquqMRd7HsN3aP4cG8tZEbFz8ri2zT5V0us16bR91Uz4vjr4zKv890onn+vKtvB6pWW8\nLuenvvwKMBIRO0XErAF1u+86/Dt/iubssF/J0HYtDlgT+xrw6aI70W8RcWNEtBbNXAhsXmR/+qCT\nBVprISIei4g7k+f/RXOGuMqtN9Sp5D9B9gO+XXRfzHrQbrHntUTEzUC7NRbqNhlGr9eko/YV1Mn7\nmugzrw7fK/1aeL3qerku0PzeqGM+mPC6RMR/RsRimv9R3VXb0ep4AXMjaS7wUET0ZyGi8joEuKbo\nTuSskwVaa0fSlsCONGcqq6vWf4L4hlKrsjfG2Is9d+OoZLjPt2syHK7Xa5LHNS2jTt7XRJ95dfhe\n6dfC61WX5bqkF34P4AZJiyR9tG+9HLxe/s67bjv0swhKuoHmGjGrN9H85voc8FmawwPTX6uscd7r\nCRFxVbLPCcDKiLi0gC5ajiS9DvghsCCpZNWOpP2BxyPiTkkjVPzfqNXbBJ83o3X7HwbnAackU8Of\nCpwFHJqpowPU52uSd/uB8fdK3/gzYmK7RcSjkv6IZtBamlSJrQtDH7AiYu+xtkvaHtgS+KUk0Rwy\nt1jSrIh4YoBdzE2799oi6WCaQ632HEiHBusRYIvU682TbbUkaX2a4eriiPjpRPtX2G7AXEn7ARsB\nG0u6KCIOLLhfZusY72dwMknDtGTNss2Arj5nIuLJ1Mv/DVyVsZsD1c9rAvTavjA5XJe2n3lV/V4Z\nQyef64/QXBR89D4bdNC2qnq5LkTEo8mfT0r6Cc3hcXUIWL38Hth1Ww8RbCMi7o6IzSLirRGxFc1y\n4E5VDVcTSWYR+jQwNyJeKro/fbB6gVZJG9Bcc6bOM879H+DeiDin6I70U0R8NiK2iIi30vw7vcnh\nyiqqtdgzrL3Y81jEqP+JT37Rbvkr4O48O1eQnq5Jl+2rpJP31fYzr0bfK518rl9Jc4FvlFp4vcO2\nVZX5ukh6TTL6BUmvBd5Hdb8/Ruv27zz986T775eI8KODB/BrYGrR/ejj+1tGc6bE25PHeUX3qQ/v\ncQ5wf/Jejyu6P318n7sBq4A7gTuSv885RfdrAO/7PcCVRffDDz+yPICpwI3Jz6jrgdcn2/8Y+KfU\nfpcCvwNeAn4LfCTZfhFwV/Lv/gpgWtHvqQTXZMz2VX90cV3G/Myr0/fKWO+R5qx4H0vt83XgQeCX\nwM4TXZ86PLJeF2Cr1O8OS4btutAclvsQ8CzwdPLz5HVZvl+80LCZmZmZmVlOPETQzMzMzMwsJw5Y\nZmZmZmZmOXHAMjMzMzMzy4kDlpmZmZmZWU4csMzMzMzMzHLigGVmZmZmZpYTBywzMzMzM7OcOGCZ\nmZmZmZnlxAHLak/SNpLeWHQ/zMzMzKz+HLCskiRNk/RFSad1sPvHgOf73SczMzMzMwcsq6SIeBy4\nFXjHePtJejUwKSJeTG17vaSTJb0o6XpJR6W+9j+S7d+TtHPf3oCZmZmZ1dL6RXfArAc7AjdOsM8B\nwE/TGyLiWUnnAf8LODwifgMgaSowDdg2In7bh/6amZmZWc25gmVVticTB6z3RMS/jrF9b2B5Klzt\nBrwvIr7hcGVmZmZmWTlgWSVJ2gh4c0QslbS/pK9JekGSUvv8CfC7Nod4L3CDpEmSvgi8NiIuG0DX\nzczMzKzGHLCsqt4NLJP0d8DtwLHAOyIiUvt8GPhem/Z7Ab8CPgrslxzPzMzMzKwnDlhWVXsCL9Ic\n6rdzRLwyxtC+t0bE8tENJW0LvAn4VUR8EzgDOCKpio1J0taSFufWezMzMzOrJQcsq6o9gU8DXwAu\nBpC0feuLknYFbmnTdm/gjoj4cfL6cprTuB82zvmeAu7psc9mZmZmVnMOWFY5kiYDm0fEMuD/seY+\nq71Su/0N8IM2h3gvqckxImIVcDZwjKS1/k1I+qikfYFTgRvyeQdmZmZmVlcOWFZF2wHXAETEE8DN\nkj4O/BOsXvtq/Yh4Id1I0i6SvgS8D5ghaU6yfVNgF2AL4HJJ05Pt+wGbRsQ1wGta5zQzMzMza0dr\nzwlgVn2SPgg8ERE/6/E43wC+FRG/lHQFsCAiVuTSSTMzMzOrJVewrI727DVcJX4CvEvSXGA5zcqZ\nmZmZmVlbrmBZrUjaBDgyIr5UdF/MzMzMbPg4YJmZmZmZmeXEQwTNzMzMzMxy4oBlZmZmZmaWEwcs\nMzMzMzOznDhgmZmZmZmZ5cQBy8zMzMzMLCcOWGZmZmZmZjlxwDIzMzMzM8uJA5aZmZmZmVlO/j8+\nLLoXxm04FwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9865d1ab90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,5))\n",
    "\n",
    "ax1 = fig.add_subplot(121)\n",
    "cax = ax1.contourf(k_ACC*Rd_ACC[1], l_ACC*Rd_ACC[1], w.imag[0], 20)\n",
    "cbar = fig.colorbar(cax, orientation='vertical')\n",
    "ax1.set_xlabel(r'$k/K_d$', fontsize=14)\n",
    "ax1.set_ylabel(r'$l/K_d$', fontsize=14)\n",
    "ax1.set_title(r'$\\sigma$', fontsize=18)\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax2.plot(np.reshape(psi[:, 0], (len(zpsi), psi.shape[-1]**2))[:, np.nanargmax(sig.imag)], -zpsi)\n",
    "ax2.set_ylabel(r'Depth [m]', fontsize=12)\n",
    "ax2.set_title(r'$\\hat{\\psi}(z)$', fontsize=18)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### w/ lateral viscosity ($A_h=10$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zpsi, w, psi = baroclinic.instability_analysis_from_N2_profile( -zN2_ACC.values, \n",
    "                                                                   N2_ACC.values, f0_meta.sel(Latitude_t=ACC[1]).values,\n",
    "                                                                   beta_meta.sel(Latitude_t=ACC[1]).values,\n",
    "                                                                   k_ACC, l_ACC, z_t.values, u_ACC.values, v_ACC.values, etax, etay,\n",
    "                                                                   Ah=1e1, num=2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f9864e16310>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NkNNySUQeABCRB4FXTqMRqvqq9huSwKeBv7/qzxSRjBS2z6vqU/3ita+PG7Xj\nNNbHgqpeJV3V9hgrXB/rCv5tcV6/iEz6b3dE5Bzwi8B31tkE9m87fhn4cD//IeCp69+wjnb0f1QL\nH2A96+SzwPdU9VNLy05jfbyhHeteHyJy72JzQkTGwHtJ+xtWtz7WuNfyMdJe078EPrbOPaZLbfib\npCMKzwMvrrMdwBeAvwZq4IfArwJ3AV/r18vTwJ2n1I4/Ab7dr5v/RNq2XGUb3g2Epf+Lb/V/H3ev\nc33cpB3rXh9/r//sF/rP/Vf98pWtDztl15gBGuLOPWMGz4JvzABZ8I0ZIAu+MQNkwTdmgCz4xgyQ\nBd+YAbLgGzNA/x+IPNQMiCPSiQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f986524ac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sig = w[0].copy()\n",
    "for i in range(14):\n",
    "    sig[:, i] = np.nan\n",
    "for j in range(14):\n",
    "    sig[j, :] = np.nan\n",
    "\n",
    "for i in range(-1,-14,-1):\n",
    "    sig[:, i] = np.nan\n",
    "for j in range(-1,-14,-1):\n",
    "    sig[j, :] = np.nan\n",
    "\n",
    "plt.imshow(sig.imag, origin='bottom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cZ7GCLFUUFMmNm2+GQw+Fj30MvvtdGDUq7RGJiMRTzAALNgmyGqkOrkDVAguh\nHKdtMssSkwiyej2LlSplsUS64lvfglNOgeuugw9/OO3RiIgko7gBFgwbZFUHV33KXCWv0XTArO/j\nlVCQlYQiBFlxKIslUkzucOGF8O1vwx//CIfrQw0RKZBiB1jQMMiqDa6kYMpx2xdrPVaacpvFEpGO\ncIdzz4Xrr4fbboM99kh7RCIiySpOgDVhdOOjRh6Cq0rBjUnjBzZWtyuxsUpe1rNAnVDu8PUdkJX1\nWMpiRaBP1EUSt349nH02/O53UC7DmDFpj0hEJHnFCbBKrRxrcxFc1cptkNWoUEWrBSzSLkWv9Vg1\nY4ixRYGyWCI9b906OP10uOeeoFLg6Iz/CRMRiaqHAqx8BlcVhQmy2g2aaq8vt3n/dq/fpH2xgqw0\npVa2XVkskdStWQMnnQTLlsGNN8L226c9IhGRzilQgLW26ZHH4Kp2s9ncB1lRM1Jxg6y4ClT0Is9T\nBUUkn15+Gd7zHnjpJfjVr2CbbdIekYhIZxUmwJo0fqDpkbfgqqlKkAX5C7KSal9uo20713ZQUdZj\nxbq3slgiPeXFF+Gd74Sttw6KWmy5ZdojEhHpvMIEWH0MNj2KElxNGh/+DKW1G4OrytesB1lxpRlk\naapg1f2zESiKSLY99xwcfTSMGwfXXgubb572iEREuqMwAVY/A02PIgRXFQqyqpS7eO8CBVl5LXih\nfbFE8mHNGnjve2G//WDmTBgxIu0RiYh0T2ECrDNX/XDDZsH1jryqXYdVoSArgnIC91aQJSLSlDv8\n8z8H0wGvuAI2K8w7DRGR1mRn9X4C+lfd1/K1UT9F72MgM1OkJo0fCN6ol9YCI4MAokTwdcLo9Muc\nd0uZjcFlktc27GPk0HVwPWqQ/sgfXgyMPrCtf6/V/FCw2yM1DbJY8yK2FZGWTJ8ODzwQ7HU1slDv\nMkREWqNffUVQm1UpUfwga8mqoZm6MvEDpy5bsLR/YyYyokH6GmY5WzFAP/05zvDmkqYqSoH94Afw\n4x/DH/6gaoEi0ruUuM+xBUv7NwZX5ZonS+HXCaPjH0VTTqIPTRUM7p3DtVgi0hG//S1ccAHMnQtj\nxqQ9GhGR9CjAyrhGb56bBlcVJZLJ6ijIatBHdoIsaYMySCKJu/tuOPnkoBT7vvumPRoRkXQpwMqh\nloKraqUIRy0FWQ36yEaQpSyWiKRl6dJgr6srroBD9e9SREQBVt60FVyViJ7FKrFp+6wHWeW07puN\npYxp7o/i1O9IAAAgAElEQVSVlcIvLVMWSyQRq1fDscfCpz4VlGUXEZGMB1hmNs7MbjWz+83sXjP7\neNpj6qbaN8xDshzlYRqX6nw/3NFKf0Vbl1VOqp/4QVbaUwXTKtuuLJZIPq1bFwRVb3sbfPKTaY9G\nRCQ7Mh1gAWuB/+fuBwBvAj5qZm9IeUyp2PDmuzyyxczV2qFHK0pNDhgahGUhyEqqQmI5mW6S0KtT\nBVOhLJZILF/5CqxdC1//OpilPRoRkezIdIDl7ivcfUH4+AVgITA23VF1X93gqkz9wKBE/YCqNuBq\nNwAr1XyF5IOsNIO2chJ9FGM9VhriZLFEpPv+8Ae47LKgJPuIEWmPRkQkWzIdYFUzsz2AScAd6Y6k\nuxoGV5XsTeVcicbBVSvazXYlLQsZsSRkZD1WHHnLYmmaoEh3rV4NJ50E3/sejBuX9mhERLInFwGW\nmW0L/Bz4RJjJ6hkbNqItrR2aRZowus4UvojBUXnk0KPuNXXOJbmJ8ZJVyfRXTqntkH7SX4+lLFaL\nNE1QpC3ucMYZQdXAqVPTHo2ISDZl/uN2MxtJEFz9yN1nN7pu+oyNj0uHQunvOz+2bpk0fiB4w11a\nC4SZrFL45IavMYKrYa+p8zjJ4CpLyiSzd1h5ZOxs4IKl/RsD7AgG6aOPwUhtB+inn2j3HqSfvoht\no/JDwW5Prr/yY8EhIkPNnAkPPgjXXJP2SEREssvcPe0xNGVm1wBPufv/a3KNr3+6vX7jfFoedRpU\nu1OvGlYRLNcGWQkEV+VWrg+/Zim4ajS1sBSz37jtN/QTf8plnCAraoBVETXIihNg9a+6L1K7WAHW\nvGH6vgjcPfYyfjNznxOh3XHJ3F/yw8w8a3+fFy6EN78Zfv972H//tEcjIpI8M0vk722mpwia2aHA\n+4C3mtndZnaXmR2d9ri6pfbNcf3pgjGDqzL5Da6aKac9gGxIa6qg1mJ1npkdbWYPmtnDZnZug2su\nM7NFZrbAzCYN19bMdjCzm8zsITP7rZltX/Xc+WFfC83syKrzXzKzpWb2XM29TzWzJ8Pf23eZ2QeT\nfQWkm15+GU44AS6+WMGViMhwMh1gufvt7j7C3Se5+xvd/RB3vzGJvqN+Sp62IdmMJIKrus/XOSA/\nwVUSykn1k+/1WGnsjaWKgsMzs82Ay4GjgAOAE2u3sDCzY4C93H1v4Ezguy20PQ+4xd33BW4Fzg/b\n7A8cD+wHHANcYbahMPcc4G8bDHVW+Hv7EHf/QfyfXNLymc/AvvvChz6U9khERLIv0wGW1Ddp/EBy\nwVW5zlFPVoOrZuMqx+w7bvsN/aQfZKUhV/ti5a/YxWRgkbs/5u5rgFlAbcmBqcA1AO5+B7C9mY0Z\npu1U4Orw8dXAtPDxcQTB0lp3fxRYFPaDu9/p7isbjFPTGgvg5pth9uygaqD2uxIRGZ4CrIxrtIYm\n0rqcesFVK7IaXLWinHL7Df2kW0+mV7JYPTRNcCzweNX3y9h0j8BG1zRrO6YSLLn7CmCnBn0tr3O/\net5tZn8ys+vMTAW9c+jVV+FjH4PLL4fXvjbt0YiI5EPmqwhKY82CrE0yHo2CqzwHT91SJrmiFzHE\nrSqYhjQqCkY2hWGLXeRclNxDnCoLc4CfuPsaMzuDICP2thj9SQq+8Q2YODEoyy4iIq1RBqug8vZG\nvKPKaQ8glPJUwbxlsaIqQharfC9M/8nGo47lwPiq78eF52qv2a3ONc3arginEWJmOwNPDtNXQ+7+\nTDgFEeD7kMPN2XrcsmXw1a/CpZemPRIRkXxRgNUrKmu2Sgz9OmF046NIyim339BPfqcKRr9nxG0N\nClrswg8d/jj8w/CFb2886pgPTDSz3c1sFHACQcao2hzgFAAzmwKsDqf/NWs7BzgtfHwqMLvq/Alm\nNsrMJgATgTtr7jckQxYGaBVTgQeGfXEyyMzea2b3mdk6Mzuk5rlGlRUPMbN7wiqNl1adH2Vms8I2\nfzSz6kA3c/7lX+AjHwkyWCIi0joFWL2kUZDVSLPgq8jBWCPlpPqJF2SlVfAiT1msyHJS7MLd1wFn\nAzcB9xMUoFhoZmeG0/Fw97nAEjNbDFwJnNWsbdj1DOAIM3uIYDrfV8I2DwDXEQRJc4GzKps0mdkM\nM3sc2Cos1/75sK+Ph4HJ3eH9TuvcK9JR9wLvAm6rPmlm+9G4suJ3gNPdfR9gHzM7Kjx/OrAqrOx4\nKXBJF8Yfye9+B/Pmwfnnpz0SEZH8yfxGw62IstEwRP+EvFsbDW+8X7SsQ8M34vXKtJfrXNeutNZz\ntRPglRK4XxJ9QOxNiNPYgLgnNh6usw4ryY2Go/yu2ux12mg4bWb2O+BT7n5X+P15gLv7jPD73wDT\ngceAW919//D8CcDh7v4RM7sR+IK732FmI4AV7v76BvdLbaPhNWtg0iT4t3+Dd787lSGIiKSiJzYa\nlg6pzWRVHrdyNKNMVuFF/5AgR9mvnGSxJHWNKiuOJajMWFFdpXFDmzCTuNrMMveL81vfgnHj4F3v\nSnskIiL5pACrV0XNnpQaHBVpBFntZM7KCd0ziX5SnCqYxlqsbitCsQvpDjO7OVwzVTnuDb92unZe\n5rKSf/kLXHQRXHaZ9rwSEYlKZdp7XWmY58tt9FO5dsLobJd/L5OJsutAEGTFmCqYRun2AfojTRWM\nWrJ9YPSBkacJirTC3Y+I0KxZlcZGFRcrzz0RThF8jbs3/GU5ffr0DY9LpRKlUinCMNvz2c/C6afD\nvvt2/FYiIqkrl8uUy+XE+1WA1ctKa4fPopQanC83uLZyPutBVhLKJBOoxQyyohqkL/JarMIr/p5Y\nEk11TmcOcK2ZfYNg6t9E4E53dzN71swmE1RsPAW4rKrNqcAdwD8Ctza7WXWA1Q1LlsDs2bB4cVdv\nKyKSmtoPry688MJE+tUUwV5XWrvp0VI76k8RrH6c5TVZ5Yz1E0MaVQW7vRYrakEaTROUuMxsWlgl\ncQrwX2Exi6aVFYGPAjOBh4FF7n5jeH4msKOZLQI+CZzXvZ9keDNmwIc/DDvskPZIRETyTRksSV6J\nfGSyyiSUgUqgH2WxRDLJ3W8Abmjw3MXAxXXODwIH1Tn/CkFp98xZtgyuuw4eeijtkYiI5J8yWNIZ\nparH3chkLVkVLZArJ3T/JPqJUfSiF7JYXadqgtJDvvpV+OAH4fV1i8aLiEg7FGBJ55SqHndrumDe\ng6wU5KWioKYJinTGypXwox/Bpz6V9khERIpBAZZ0z4TRQ49OSXNKYjlu+3xlsUQk/77+dTjpJNhl\nl7RHIiJSDAqwpLNKTZ6rDbiSDLraDbLKyd06thSCrKhZrMIXu9A0QSm4p5+G738fPvOZtEciIlIc\nPR1gaW+dOmJufltXqY1r06w8WM5YPyIiHfbNb8K73w3jx6c9EhGR4ujpACuqKJul9rxSG9cmFWTl\neT1WD2SxRCRdzz8PV1wB52WqWLyISP4pwJLuKdF6oKUgqzPZxAzRNEGRdP30p3DYYbDXXmmPRESk\nWBRgSfeVSGb/qVbluehFRN3OYolI/lx1FXzoQ2mPQkSkeIr9EblIVGW6GwQ2ktIGxO0aoJ/+CFNn\nB+nvuSm30TJwWi8qybrnHnjiCTjqqLRHIiJSPMpgdVGUN6CSkDxPFYyoyFks7YklEs/MmcHGwiNG\npD0SEZHiUYAl0kw5A/3kZC2Wil2I5MPLL8O118IHPpD2SEREikkBlshwymkPgMhBVh42H45a7EJE\novnFL6CvD/bYI+2RiIgUkwIskVaUM9JHlxR5mqBIr/v+91XcQkSkk/Ix90iyrZz2AFq0ZFW88u9l\n4he+iNNHxIIXC5b2M2l8d9b/RS120U1+KNjtaY9C4jCz37d46cvufmRHB5MzixfD/ffD1Klpj0RE\npLgUYIm0o0y6QVYXDdJHH4NdulfvVROUWP4W+PAw1xjwzS6MJVd+8AM4+WQYNSrtkYiIFJcCLElH\nOe0BxFAmvQApJ2Xbu2Vg9IH0r1IJ8x70B3e/eriLzOykbgwmL9xh1iz45S/THomISLEVZg1W1Ck/\nenMmkZRTbt+mbha7yEM1QZVrzzd3f1uL12l6YJV77w2+HnxwuuMQESm6wgRYUkBx1ks1EmU/rE4p\nR23XvcRzN4tdqJqgSGfdcANMmwZmaY9ERKTYFGCJRFVOewDtyUPJdpF2mdnfmNmtZrbKzF4NjzVm\n9mraY8uaG25QcQsRkW5QgBVRNxfkd6vQQCZlPYtVTql9xjcf7uY0QZVr73n/CdwOHAbsFx5vCL9K\naOlSePxxOFTTY0VEOk4BlvSmLAVZGZeHPbGiyPs6LDM72sweNLOHzezcBtdcZmaLzGyBmU0arq2Z\n7WBmN5nZQ2b2WzPbvuq588O+FprZkVXnv2RmS83suZp7n2Nm94f3vtnMdkv2FdhgZ+Dz7n6fuz9S\nfXTofrk0ezb8wz/AyGx/NiMiUggKsCT7OpHFguwEWXHatinr0wS1Dqs1ZrYZcDlwFHAAcKKZvaHm\nmmOAvdx9b+BM4LsttD0PuMXd9wVuBc4P2+wPHE+QFToGuMJsw0qeOQRl02vdBfS5+yTgeuCrCfzo\n9VwNqFrgMDQ9UESkexRgSfeV0x5AlawEWZHul+2PojVNsKMmA4vc/TF3XwPMAmrfPk8FrgFw9zuA\n7c1szDBtpxIELIRfp4WPjwNmuftad38UWBT2g7vf6e4rawfo7re5+8vht/OAsTF/5ka+AvxbmC27\ntfro0P1yZ9UqmD8fjlRNRRGRrsj2OzSRigmjs1UBMGllMr35cDc3He4mPzT6Fg8pGws8XvX9MsKA\nZ5hrxg7TdkwlWHL3FWa2U1Vff6xqs5z2AqbTgd+0cX07fg4sAX4JvNShe+Tar38Nb30rbL112iMR\nEekNhcpg5eGNUn8Xi2NIi7KSxYrSNkIWS9MEe1aU4twe+6Zm7wf66NwUwUnAMe5+ubvPrD46dL/c\n+fWv4Z3vTHsUIiK9Qxksgs2Ge3CKkVRbsiq5tV5lMp2NknS1EkA+XP4LD5dXNLtkOTC+6vtx4bna\na3arc82oJm1XmNkYd19pZjsDTw7TV1Nm9naCdVyHhdMRO+F/gP2BBR3qP9fc4bbb4OKL0x6JiEjv\nKFQGSwquU8UuKrKSycqoKNUEtQ4rmn1Ku/AP09+44ahjPjDRzHY3s1HACQTFJqrNAU4BMLMpwOpw\n+l+ztnOA08LHpwKzq86fYGajzGwCMBG4s+Z+QzJkZvZGgsIax7n7063/9G1bAtxkZlea2Rerjw7e\nMzcWLw4qB+6xR9ojERHpHQqwRDql3K02xZsm2E15LNfu7uuAs4GbgPsJClAsNLMzzeyM8Jq5wBIz\nWwxcCZzVrG3Y9QzgCDN7CHgbQQEJ3P0B4DrgAWAucJa7O4CZzTCzx4GtwnLtnw/7ugTYBviZmd1t\nZjd06OXYGvg1QWZut6pjXIfulyu33QaHHw4WZYKoiIhEoimCMfQxoPUi3dbpYhdJThWUyAbp7+pm\n3nnk7jcC+9acu7Lm+7NbbRueXwW8vUGbi4FNJpq5+7nAJvtwufsRTYafGHf/QDfuk1e//z0cdlja\noxAR6S3KYInUSnuqYJQ2Ij3EzLZK8roiU4AlItJ9sQIsMxs0s2vM7GQz2zGc039sUoMTqasbGaa8\nlYTv0jRBrcOSjNhk360Ghi3EUWSPPQYvvQT7bpKrFBGRToo7RfB44GngS8BbgNHAowRz9FNht+dz\nTcVw+hhs+83tpPEDxV1rk6d9scqoqqBIsrY0s2tauG7zjo8kwyrZK62/EhHprlgBlrs/AmBmv3b3\n34SPj0tiYN2mUu1dUk57AG1Icz1WmZ4Pyrq5DivHGw73qi+3eN1XOjqKjKsUuBARke5KqsjFODM7\nnyBztWtCfRZWPwNdnTZVWN3IYiUVZJXpfMBUHgmltR2+iUj63P3CtMeQB7//PXz842mPQkSk97S8\nBsvMDmn0nLtfBfwJOANYnMC4JC16g54d5c7foojrsEQEVq+Gv/wFDtTEDBGRrmunyMX/a/aku891\n94+6+y0xx5QrKiXdplLaA0hROe0B9AZN9RWBP/0JDj4YNlOtYBGRrmvnV+97zGx8oyfNbJ8ExiMi\nkooiFseR3rVgAUyalPYoRER6UzsB1snAO+s9YWabARclMqJN+z7azB40s4fNbJPNLOu20WJ1SVJe\nqhVCpHLtWaaNvCVtZvZeM7vPzNZVT5UPtyX5q5ndFR5XVD13iJndE/7durTq/Cgzm2Vmi8zsj80+\ntIxLAZaISHpaDrDc/efAf5rZCZVzZraVmX0MeAR4V9KDCwO3y4GjgAOAE83sDUnfR6Rryh2+XqTH\nhEHLGWZ2Rbgv44YjoVvcS/D37bY6zy1290PC46yq898BTnf3fYB9zOyo8PzpwCp33xu4FLgkoTFu\nQgGWiEh62pqd7e6rgMfN7Egz+yLwOMHarEuBazswvsnAInd/zN3XALOAqR24DxCUas+yPgbbbjNp\nvNaISXMqdCE5dzXwSeB5gg/7qo/Y3P0hd18E1NtNapNzZrYzsJ27zw9PXQNMCx9PDccL8HPgbUmM\nsdarr8JDD6nAhYhIWlqeT2RmZ7v75e5+u5l9GTgW+BhwnbuvM7MdOzC+sQRBXMUygqAr9wpVqr2E\nMi2SKQOjD8z8ByaSmKOBCe6+OoV772FmdwHPAp9z9/8l+Lu1rOqaZeE5qPqbFv7dXG1mo8MPLxOz\ncCFMmABbbZVkryIi0qp2FmwcF24ovAT4PPC4u/9n5Ul3fyrx0YlkRZqbDkvXaMPhXFoKbBGnAzO7\nGRhTfQpw4LPu/qsGzZ4Axrv7M+HarBvMbP92b93+aIen6YEiIulqJ8B6M7DYzB4FbgYeMbN3u/sv\nIChG4e43Jjy+5UD1IuBx4blNTP/Jxselg+DwLlYE62OgWIvxS2vzUyyhG5sNJ61MZ8vVF2zD4UH6\nu74dQvne4JBsMrO3Vn17DTDbzL4JrKy+zt1vbaU/dz+i3TGE09afCR/fZWaPAPsQ/I3arerS6r9b\nleeeMLMRwGuaZa+mT5++4XGpVKJUKrU0NgVYIiKtKZfLlMvlxPtt5130DOAK4Ajg7QTTA8ea2d3A\nLcD+QNIB1nxgopntDvwFOAE4sd6F00+qOXG7yi5nVglNKWxHmd7ePywFpYOCo+LCWcn1HW1q8A+T\nG0AxzKxzrraSrQN7JnzfDRmncFr8Kndfb2Z7AhOBP7v7ajN71swmE/wNOwW4LGw2BzgVuAP4R6Bp\nAFgdYLVjwQI49thITUVEekrth1cXXnhhIv22E2BdGs5xvzY8MLN92RhwvT2REVUJ56ifDdxEUJBj\nprsvTPo+1fpX3aeNSqXnLFja35WCKAP006/NuSUmd5/QrXuZ2TTgW8COwH+Z2QJ3PwY4DPiimb0K\nrAfOrFoH9lGCqHhLYG7V7I6ZwI/MbBHwNMGHhol74AHYv93JiiIikph2yrRvsoA4rK50ubtPI6gk\nmDh3v9Hd93X3vd39K524R1qivNFUJUHJiiiVBLtJH5T0BjOb3eD8L5Lo391vcPfd3H0rd98lDK5w\n91+4+4FhifZ+d59b1WbQ3Q8K/259our8K+5+fHh+irs/msQYqz33HLzwAuy6a9I9i4hIq9oq0z6M\n6xPsS/Km1OHrRUTqe0uD86VuDiIrHnkE9toLrCPlM0REpBXDThE0s52A5939pWbXuXv7qZUCUaGL\nFOWx0IVklioJ5kO4FyPAqKrHFXsCj3V5SJmweDFMnJj2KEREelsrGazXABea2dfM7LBODyhJepMk\nmVVOewD5UqgPLyQpu4XHZlWPdyOo2vc4QRGJnqMAS0QkfcOmKNx9MfAZM9sCmGZmVxDs//HjTswf\nzwIVuuiSEgo0OqVgpdpFarn7BwDM7A/uflXa48mKxYthypS0RyEi0tvaKXLxirv/1N3PAn4A/KOZ\nfcfMTjOzbTo3xGIrVKGLUudvkSpNQ0xEtDLlIvW5+1VmtreZfdbMvh1+3TvtcaVl0SJlsERE0hap\nyIW7P+HuX3X3jwALCaYQXmpmjRYbSx51IwNSSqifCaMT6kiKRJno4jOzk4C7gYOBF4GDgLvC8z1H\nUwRFRNIXu4qgu9/h7v8CnAvsZGZXmtkF8YeWjG6uw+rT/j6SYwuWtp9ZynqpdukJXwKOdfd/cvfP\nuPsJwLFsuvFw4b34IjzzDIwdm/ZIRER6W2Jl2qumEJ4JfDupfiVnSl1qU083sliaJtgT/NC0RyBt\n2A74Y825eUDPTV1/5BHYc0/YLMkNWEREpG0t/xo2sy+YWcnMRtac38LMhnz07e7PJjXAtPSvuq97\n98ryOqy8FUrQVEGRXvN14CIz2xLAzLYCvhye7ymaHigikg3tfM41FZgOrDSzOWZ2tpnt7e6vACPN\n7KyOjFDyp9SlNmlRFisVKtU+lJkdbWYPmtnDZnZug2suM7NFZrbAzCYN19bMdjCzm8zsITP7rZlt\nX/Xc+WFfC83syKrzh5jZPWFfl1adH29mt5jZn8zsVjPbNflXAYCzgE8Cz5nZSuBZ4BzgI2a2tHJ0\n6N6ZsnQp7L572qMQEZF2dpK9wN1vNLNtgbcCRwKfMLMRwK3AFsAVHRhjrhRuw+E8ysPGw6W0ByB5\nZmabAZcDbyPYNmO+mc129werrjkG2Mvd9zazvwO+C0wZpu15wC3ufkkYeJ0PnGdm+wPHA/sR7DN1\nS/gBmwPfAU539/lmNtfMjnL33wL/DvzQ3X9sZiXgK8ApHXg53t+BPnNp+XKtvxIRyYKWAyx3vzH8\n+gIwJzwwswnA4cBdnRhgEux2ramomDR+IFIxg64okdy+WJ0Ospas0nRESdNkYJG7PwZgZrMIZhk8\nWHXNVOAaCIoRmdn2ZjYGmNCk7VSC3+cAVxP8izwPOA6Y5e5rgUfNbBEw2cweA7Zz9/lhm2uAacBv\ngf0JMkm4e9nMZif+KgR939aJfvPoiSfg4IPTHoWIiCRRRXCJu//Q3e9JYkBZUsR1WJFEWYdVinqv\niO3qUQAkFLZU+1jg8arvl4XnWrmmWdsx7r4SwN1XADs16Gt5VV/LGvS1AHg3gJm9G9jWzHZo7cdr\nXbgO+Mtm9mczezY8d6SZnZ30vbJOGSwRkWxQrSGRqLo5DbHcvVt1Qx42Gy5g1tsitPEY9/s0UDKz\nQeDNBEHZuhj9NfIN4EDgfWwc7/3ARzpwr0x74gnYtVMr3UREpGXtrMGSFhVyHVZpLZTb/N+lRLTA\nIGq7evKwHqsVpbQHIElpZe+w58t38UK56azr5cD4qu/Hhedqr9mtzjWjmrRdYWZj3H2lme0MPDlM\nX43O4+5/Ad4DYGbbAO9x9+ea/VARvQuY6O4vmtn68N7LzayncjnuymCJiGRFz2SwurnhcDd1rVx7\nt5US7KuTUwWLELxJ5mxXOoRdpn9ow1HHfGCime1uZqOAEwjXxVaZQ1hUwsymAKvD6X/N2s4BTgsf\nnwrMrjp/gpmNCtfdTgTuDKcRPmtmk83MwvvNDu/5uvAcBMUyfhD19RjGq9R8WGhmrwee7tD9Mum5\n52DECNhuu7RHIiIiPRNgRZX1dViZV0qpbS2tx5ICcfd1wNnATQTT4Wa5+0IzO9PMzgivmQssMbPF\nwJUE5cwbtg27ngEcYWYPEVQZ/ErY5gHgOuABYC5wVlhBEOCjwEzgYYLiGTeG50vAQ2b2IMFari93\n4rUAfgZcHQZ+mNkuBFUSZ3Xofpm0fLmmB4qIZIWmCHZI1qcJRqomGGWaYFwlsj9dsMcrCg7S172C\nK7JBGMjsW3Puyprv6xZ6qNc2PL8KeHuDNhcDF9c5PwgcVOf89cD1jX+CxFxAEBjeC2wNLAKuAi7s\nwr0zQ9MDRUSyo6cyWJommJJS2gPImFLaA8ivLH9oIelw91fd/Rx33xYYQ1A2/hx3fzXtsXWTClyI\niGRHTwVYUWmaYJUoJdtj3zPBvno40yRSRGa2fzg18nyCsvD7pT2mNCiDJSKSHQqwelhXi12UUm5f\nrRNBlopdiHSVBX5AMDXwAoLNkD8L3GNm/1FVYKMnPPGEAiwRkaxQgNVBfV3MRnV1mmDULFYp7n1j\ntpeeFXWz4QLuhVUkZxD8Vpji7ru7+5vcfTzwJoJ9t85Mc3DdtnIl7LTT8NeJiEjn9VyAlYd1WJmf\nJpimUkL9KIslkncnAx939/nVJ8PvPxk+3zOefhpe97q0RyEiItCDAVZU3VyH1U2RpwmmlcVKqg8R\nybv9gdsaPHdb+HzPWLVKAZaISFYowOqwwk4TjKOUkT5EJM9GuPvz9Z4Iz/fU3zdlsEREsqOn/gBV\naJrgUF3PYiWlFLO9KgqK5NnmZvYWM3trvYMe2+fx6adhtH6liYhkQk/9AYqrf9V9kRfLd0sfgwzS\nl/YwhlciuQ2E86SU9gCyY4B+rTeUOJ4EfjDM8z3hpZdg3TrYZpu0RyIiItCjGaxu6+Y0wahSyWKV\nojdNtA8RyR1338PdJzQ70h5jt1TWX/VWYXoRkexSgJVhPfHpfikjfWRZKe0BiEiWrVql6YEiIlnS\nswFW1HVYeagmGLXYRW7XYkH0IETrsEQk51TgQkQkW3o2wOq2PEwTTE0pY/1Ipg3Sn/YQRDJFAZaI\nSLYowMq4qNMEc5fFKsVrnng/IiE/NO0RiDSnKYIiItnS01UE7fZob57yUE0wFaW1UI7xv1SJZCoL\nttvPhNGwZFUCNybop9m0w3L4tdRGn+Wa79tpKxsMjD4wE1N8FyxVBk6SpQyWiEi2KIPVRVGnCXa7\n2EXkLFaWlNq8Psm1WK0Ea+Wqo13lOkfd63r68xORnlGpIigiItmgAKvAok4TjCUrUwWj6HaQlaRy\nd28nItmhTYZFRLKlOAHWvGjNul1NMC/FLmJlsbISZEXpJ40gqzzM86UW7zdcPyJSSM89B9tvn/Yo\nRESkojgBVsF1u9hF6koZ6yeqJas2PeopJ3S/2n40TVCk8F54AbbbLu1RiIhIhQIsaSjVLBakF2R1\neuXPEiwAACAASURBVG+sRgFXOWJ/5RhtRST3XngBtt027VGIiEhFsQKsgk8TzF0WS0FW55XrPC5v\ncpWIRGRml5jZQjNbYGbXm9lrqp4738wWhc8fWXX+EDO7x8weNrNLq86PMrNZYZs/mtn4JMb4/PMK\nsEREsqRYAZYkrhAVBaOaMLo7gVaULFa55tpKH2WGfgVNExSJ5ybgAHefBCwCzgcws/2B44H9gGOA\nK8zMwjbfAU53932AfczsqPD86cAqd98buBS4JIkBKoMlIpItCrBCymJ1SJ6zWN3ULMga7nsR6Rh3\nv8Xd14ffzgPGhY+PA2a5+1p3f5Qg+JpsZjsD27n7/PC6a4Bp4eOpwNXh458Db0tijAqwRESypXgB\nVsRpgtJY7CyWgqzWtJLJanS+kmkrMfQrJPP6iwjAB4G54eOxwONVzy0Pz40FllWdXxaeG9LG3dcB\nq80sdppcAZaISLYUL8DqAWlksTIxVbCUUj/dXI/VTiarVqnmq4i0xMxuDtdMVY57w6/vrLrms8Aa\nd//PJG8dt4P16+Gll2DrrZMYjoiIJEGLM6rY7eCHtt+uf9V9DIw+sO12fQwwSH/7N4yhj0EG6evq\nPYEgi5LEWqASxZ8it2TVxqCuTGs/c6nm64bzyl6JDMfdj2j2vJmdBhwLvLXq9HJgt6rvx4XnGp2v\nbvOEmY0AXuPuDTfMmz59+obHpVKJUqm0yTV//StstRVspo9LRUTaVi6XKZfLifdr7p54p91mZu4X\n1JycEq2vKAEWECnAAmIFWAMR28YJsBYsjRkQJlVwodzlPlrdNDhJrWbOSg0eQ9sBVtRMZdTsaPRs\nbMQ1jBHXTG72OnD32NkGM3MeW9N+w9033+T+ZnY0QaGEzYCZ7j6jzv0uIyjA8CJwmrsvaNbWzHYA\nfgrsDjwKHO/uz4bPnU8wRW4t8Al3vyk8fwjwQ2BLYK67f7Lq/scDXwDWA39y9/e3/8OnK3ytvgYc\n5u5PV53fH7gW+DuCqX83A3u7u5vZPODjwHzg18Bl7n6jmZ0FHOjuZ5nZCcA0dz+hwX29lb/PK1bA\npEnBVxERicfMEvl7r8+8EtLtYhdxpDpVMEsZlVLaAxhGK0FdqcFjyNZrLYkys82Ay4GjgAOAE83s\nDTXXHAPsFVasOxP4bgttzwNucfd9gVuJUTHPzCYC5wJvcveDgA2BV858C9gWuNnM7jKzKwDc/QHg\nOuABgnVZZ1VFRB8FZgIPA4vc/cbw/ExgRzNbRPB6nBd3cFp/JSKSPZoiWCPqNME09DMQOYuVqiSm\nC5Yo/lRBGDpdsFapwWPpBZMJ3rg/BmBmswgq1D1Ydc1Uggp2uPsdZra9mY0BJjRpOxU4PGx/NcG/\nsvOoqpgHPBoGCJPN7DHqV8z7LfDPwLfd/blwDE8l/ip0QRigNnruYuDiOucHgYPqnH+FIFBNzAsv\nwHbbJdmjiIjEVdwMVo6qCfZcFisppYz00Wn1MlmlBo+lV9RWsKuuVDfcNc3ajnH3lQDuvgLYqUFf\nrVTM2wfY18z+18z+ULUXlCRIGSwRkewpboAVQ7f3xIoj6hqWuDIzVbCUTDeZ12i6YKnB9ZoeWFfU\ntZIFEWVOeZxFuiOBicBhwEnAVWb2mhj9SR0KsEREsqfYUwTnEbnYRbf1VEXBiqxUFozbvlvqTRcs\n0ztBZlG08v/8wjI8WG52xXJgfNX31ZXqqq+pV81uVJO2K8xsjLuvDDfMfXKYvppVzFsGzAs36X3U\nzB4G9oa0dj0vJgVYIiLZk9kMlpldYmYLzWyBmV2fl08+lcVqU1YyWcO17+ZeWM1UMlnlqnPlOtcl\nVa1R0rFfCd41feOxqfnARDPb3cxGAScAc2qumQOcAmBmU4DV4fS/Zm3nAKeFj08FZledP8HMRpnZ\nBILM1J3hNMJnzWxyWPTilKo2NwBvCe+/I0Fw9ef2Xwxp5oUXYJtt0h6FiIhUy2yABdwEHODuk4BF\nhNWsuiXqNME48rYWCzK0HquX1AuypKe4+zrgbILfk/cTFKBYaGZnmtkZ4TVzgSVmthi4EjirWduw\n6xnAEWb2EPA24Cthm7Yr5rn7b4Gnzex+4L+Bf3H3Zzr1mvSql14K9sESEZHsyMU+WGY2DXiPu5/c\n4PlN98Gq1uU9sSBf+2IF901xbyzIzv5YzdqnsRdWM5WsWomhX6tpH6xN7xkhy5zoPlhXR/ide2oy\n+3JIfrS6D9all8KSJfDNb3ZhUCIiBddr+2B9EPhNt2/aK1msuHpqqmCW1Gayyg2uE5HCevVV2GKL\ntEchIiLVUg2wzOxmM7un6rg3/PrOqms+C6xx959EvlEKJdvzthYrE1MFsxJk5clwQZbWYokU2iuv\nKMASEcmaVN99ufsRzZ43s9OAY4G3DtfX9N9vfFzaPTiSkMbGw2lUFAzuG6+q4KTxA/GnC2ahsmCc\ntmmoVBcss3HspRTHUwDl/4VyChlskXa98gpsuWXaoxARkWqZ/XjbzI4GPg0c5u6vDHf99MM6P6Y8\n6Gcg1lqsTEgqyIo1BuIHWZU1Ut1cu1WmJ4KsQfo7Op229PfBUfHFSzp2K5FYXvn/7d1/tCRlfefx\n94cZWH+g6AiOJ+DoKD+EoI7MBNnFE3pREHCDbLIqJlk1awxxJJrV4woaV5OYBNizC8EEXc9y1l94\n0N31ByoguNjm4AZkBkdRBxncAQRkWBggEQ3OMN/9o6vn1u3b3be7q7rrqerP65w+07e6nqqna+7c\n6c/9Ps9Tj8EBB1TdCzMzy0t5DtaHgf2BayXdLOmSQker2TDBquZiJTFUsCytqjswQ/1CXLv7Z7K/\nRzGzgjxE0MwsPckGrIg4LCKeExHHZI+NVfWlisUuiih6X6wkQlaK87HGvRfW9p2zrV4Nu0fWiCGr\nlBUhzWxmHnsM9tuv6l6YmVlesgFrKuaoilXVzYe7GhuyUrdcyBr0yHHIMqsPV7DMzNIzXwGrgLpV\nsYoqWsUqTVkha54MC1mD9AlaZpY+Bywzs/TMX8ByFWuMcycwVBDKCVmtGbVJxaCQNewBe0OWq1hm\n9eCAZWaWnvkLWAXMWxWrDF70okL9QtYw3f0cssxqwzcaNjNLjwPWjMxrFQs8H6tS+ZA16gMcssxq\nwhUsM7P0zGfAKjBMsI5VrBRCVik8H2sy465k2O7+6ZBlljqvImhmlp75DFgVqeN9scpS+/lYdddd\nMn65Rzvbf++fXvjCLGWuYJmZpWd+A1YNq1geKpiZZcjq3W/ce2HVzYCQ5SqWWZocsMzM0uNfT8/Y\nhp3fZ9Oqo6s5N5vYxOQflNezmc2sL9SHdWs21e/DeovFC0WsXTXbGwhbqfr9+ytSXR5be3ansuZz\nwDIzS8/8VrCgsirWPA8VhIQWvWgV2Lfplaw5spkNlf3Sw6yoX/7Sc7DMzFIz3wFrDqUwVLA0DllW\n0GY2sIkNDllWW7t3w0qPRTEzS4oD1hxWsVIIWUnNx7K5tBCu1u8NWWZ18/jjsGJF1b0wM7M8Byyb\nSKNCVqvAvkWrWK6CVSIfrrbctRCyXMWyutmzB/bx/+RmZknxj2VwFatCpYWsoloF9p00JHXbOWTN\nVG+46q6SuJn1HipoteMKlplZepoTsL5ZdQdmr+qQVdZ8rGQWvRjrfD1fjxuSevdfu8pBawb6hSva\n7L3fl+djWd04YJmZpac5AatCVVWxUtCoRS9aBfcfNSA5SFVicLjqPPJDBR2yrC4csMzM0tOsgFWk\nilVgmGCVqq5idfrQoPlY1tFeuim1+5eVsihFK/cwqyEHLDOz9DQrYFWoyipWCiGrDEmErFY5XRgq\n9ZsUd/vXZiFoZUPoBoWsSW9AXeTG153zjt4+W8aC9WzufK+1dmcPoLWbdWs2dWdhdfateXXZ5oMD\nlplZepoXsGpaxar7h7mk5mMV1SrQtinD/4aErFRMEs56Q1Y3aDlcWV05YJmZpad5AatCRapYRaVQ\nxUoqZNVxqGBqla0BISuFoYJF7l+VD1ndoOVwZXXlZdrNzNLTzB/Lc1rFalLIKsUshgqOul+v1MLU\nIGOErEmHCY6rG6665ywSsjrP6xmuJJ0i6VZJt0l6z4B9Lpa0TdIWSeuWayvp6ZKukfQjSV+TdEDu\ntXOzY22VdHJu+zGSvpcd66Lc9rOy7d+R9HeSXlD+VTBXsMzM0tPMgFVURffFSkEqIav2QwXHlXLg\nSqiSlV8FsPsoGrJqGq72Af4GeCXwq8DrewOMpFOB50fEYcBZwEdHaHsO8PWIOAK4Djg3a3MU8Frg\nSOBU4BJJytp8BHhzRBwOHC7pldn2yyLiRRHxEuA/AReWfBnmXkSnguWAZWaWluYGrJreF6vqKlZK\nGj9UMOVQ1SuBkLVoifXs/PmbBE8asuoWrjLHAtsi4s6I2AVcDry6Z59XA58EiIgbgQMkrV6m7auB\nT2TPPwGckT0/Hbg8InZHxB3ANuBYSc8CnhIRN2X7fbLbJiJ+luvL/sCe4m/b8vbsAanzMDOzdDQ3\nYBVVYRWr6g97qVSxIIGQ1Sp++sbIB8L2wtNZhKz+4Yq9NwkuErKq/vc2oYOBn+S+vjvbNso+w9qu\njogdABFxH/DMAce6J3esuwf1Q9JGSbcD5wFvH/G92Yg8PNDMLE3NDlg1rWIVVUYVyyFrRrbvrF8l\nq509b7Nk+fZpzMNaenPgxTcIniRkbdj5/b2POTJJnSOKnDAiLomIQ4H3AO8vcixbygHLzCxNaa27\nnJobgOMma6pvQRw/+ak37Pw+m1YdPXH79Wwq50asBa1n88wWP5iaFn1vvDvy602zfSewKve+V0Jr\nN1vu2sC6NZsW/X2PErL7LbfeDfhLKlet3Z2A1crt3KLvUutDz5n7tzXzkNUeYZ9ftOGfhu54D7Am\n9/Uh2bbefZ7dZ5/9hrS9T9LqiNiRDf+7f5ljDdre67Nkc8DqRtIFwG8AjwE/Bn4vIv5B0nOArcCt\n2a43RMTGrM0xwMeBJwBXRsQfZ9v3ozOMcj3wAPC6iLhr0r55/pWZWZqaH7C+CZxQzamLhqyqbWBT\n4RvBlmXdmk3Fh6J1P5xbccuErK5xwnU+jHW/7xaFq6691ciVe78eJ1z12rTq6PQqWU9sdR5dD/9p\n7x43AYdmH/J/CpwJvL5nnyuAtwGflXQc8HAWnB4Y0vYK4E3A+cAbgS/ltl8m6UI6QwAPBb4dESHp\nEUnHZn16A3AxgKRDI+L2rP2/Am6b5FIk4BrgnIjYI+k8Ogt/nJu9dntEHNOnTXfhj5skXSnplRHx\nNeDNwM6IOEzS64AL6Fz/iTz+uJdoNzNLkSIKjQBJgqQYGmSKBqwJq1hQPGAVqWIBpVSxyghZZVWx\nSpnvM2nIao/4Wp2G/BW1dtVCNanFyEMxRx32Ocrfd5FwBZ1/I8stdLHPMyAiCi8lIClYO8HP3O1a\ncn5JpwB/TWeo96URcZ6ks4CIiI9l+/wNcArwKJ3Ky82D2mbbVwGfo1OVuhN4bUQ8nL12Lp2AsAt4\nR0Rck21fz+JqzTuy7RcBrwB+CTwEnB0RW8d/8+mQdAbwWxHxb7OA+pWIeGHPPs8CrouIo7KvzwRO\niIi3Sroa+EBE3ChpBXBfRBw04Fyx3P/PDz0Ea9fCww+X8ObMzAxp6f+3Ex1nLgIWFAtZBQIWOGQt\n9KPmIas94mtlB6y1q9IObb0hq9cU578VCVfdfxeb2LDsUu0pBiybPUlX0FlN8TNZwPo+nRUVHwHe\nHxHXZ4HzryLi5KzNy4D/EBGnS7oFeGVE3Ju9tg14aUQs+Qc+SsB64AE44gh48MEy36WZ2fwqK2B5\nvNQoCszFSoHnY5WkxWjzZ8oMRGtXlX/MsvUOF2z1vD5pxXCZYFY0XOWHIe61qrarCloBkq4FVuc3\n0Vng430R8eVsn/cBuyLiM9k+9wJrIuKhbM7VF7P7hY116iL93rPHQwTNzFI0PwGrxnOxii54UYay\n5mOVEbIqnY/VYnYLWnTDVf7ruoSsolpki1n0D1n5cDWubrjqfh9uuWvD4iUfHLLmTkScNOx1SW8C\nTgNOzLXZRWfoIxFxs6QfA4czfOGP7mv3ZkMEn9qvetX1wQ9+cO/zVqtFq9Xq6bcDlplZEe12m3a7\nXfpx52eIIFQ6Fws8VHChH4kMFSxzLlbvtqJBqDdclXnsaRrWbxj/vmItloSsbrgCxq5e5cPVontq\n9VkoIx+yPERwfmXz1f4z8OsR8WBu+4F0FqzYI+l5dH6N98KIeFjSDXTu+3UT8FXg4oi4WtJG4OiI\n2JjNzTojIvoucjHKEMH77oMXvxh27CjjnZqZmYcITqLCKlYKyhgqWEYlq6yhgoUrWalWsZYLKalX\nsob1vz3GsVrdNguVrFLDVffvvg2wki0tV7Ksrw/TWdr+WkmwsBz7rwN/JumXwB7grO6CIHRWb/w4\nCwt/XJ1tvxT4VDb36kEKrCDYJcduM7PkzFfAKqrgXKwmDBUsS+1D1pLjUE7o6hdO+h079ZBViuxa\ntFgyXLDUcNXu7uWQZUtFxGEDtn8e+PyA1zYDL+yz/THgteX1rawjmZlZmeYvYNW8ipXCDYhTmo9V\nmRbTqWL1qwD1O0+q4apM3XldsDdk9QagSZZl36vNwrXt/jkgZHUWizNLjytYZmbp8fTYcd1QrLm+\nVU43iij0oTQzyeIC/eRvLjupUe+pNNAUlxG3grbv7AlCK9lyV6cS1alIlbQ6Zv480zqHmZmZzYX5\nDFjfLNi+4pBVxpCllEJWGSoJWa1ip7QRJRCyzFLkIYJmZmmavyGClpxk5mNZuqY9XLDfefoNFzRL\njIcImpmlZz4rWOAqFmlVscoYKlhYKkMFl5tfNQ/zr/qpsJJlliJXsMzM0jS/AasBHLKWmu/5WFdV\n3YHpqyhkmaXKFSwzs/TMd8CqeRUL0llCulHzsSxtvSELioWsQRXBPucxS4krWGZmaZrvgAWNCFlF\nlTJ3hXJClocKFjUHVSwYWGGaWiXLLFGuYJmZpccBqwFSGSpYFg8VZHhVpa+rBjyfL1MJ6GXPedu+\nc/yHWR+uYJmZpckBCxpRxUolZDVqPtYoWlM8du8Ha3/QXrB2Vefat4DW7kV/1xvYlNQvDMymyRUs\nM7P0OGAlIoWhgmVpTMiquooFBUJVg6tYPeGqaz2bHa7MzMysco0JWDuKBpSKq1hlSKWKBWktejEz\nrSkdd9lhYg0OU73y4Sqzbs0mhyubSx4iaGaWpsYErCR4qGDpXMWyvXrDVTY00OHK5pmHCJqZpadR\nAavyKlYJUhkq6PlYdTGsetWgypbDldkSrmCZmaWpUQErCQ0ZKliWlELWxFzFqtYy4cpsnrmCZWaW\nnsYFLFexOlIaKpjKh+DmDRUcpUJV8yrWCOHK1SubV65gmZmlKfmAJeldkvZIWjWzkyaw4IVD1lLJ\nDhVs9dm2dhbfrqdmj2GvDXq9BnqvocOV2RKuYJmZpSfpgCXpEOAk4M5x2hWuYpUhgaGCZUnpQ2zy\nQwVbueczCVmwOEzVPFTlDVg5cTPr2cSG7PmGJY+l+2/Yu/9EZvb3aDY+V7HMzNKTdMACLgTeXcmZ\nPVSwdKnMx5p6FSv/3B/Oi9m+E9pkj5VsuasbrDohK//o6g1cC2Fsfad9e+XCMZe7z1h3iKJZglas\ngMcfr7oXZmbWK9mAJel04CcRccsk7ZtSxUolZKU0VLAME4esYVWs1oDnDlnFDAlZm1m/d7fewLWp\nrHDVKvftmJVl331hd4rTQ83M5lylAUvStZK+l3vckv15OvBe4AP53cc9fhILXnio4BKpzMeaitaA\n5w5ZxQwIWbAQtIY9CoWrJBc4AUmnSLpV0m2S3jNgn4slbZO0RdK65dpKerqkayT9SNLXJB2Qe+3c\n7FhbJZ2c235M9nP7NkkX5bbvJ+nyrM3fS1pT/lWYbytXwq5dVffCzMx6KRIcwC3paODrwM/pBKtD\ngHuAYyPi/j77x7tyX/8L4Pjs+erje/eewAkF2x9XvAtRwvvYtOro4geBvvNcxlVoTkxOvoIxifwH\n9bG0Vy7zep/ny32on1Q+vE3rHKmYNPiMG66OaMMT2vDcPaw+4F52/OmlRETh5QQkBTw4QctnLDq/\npH2A24CXA/cCNwFnRsStuX1OBc6OiFdJeinw1xFx3LC2ks4HHoyIC7Lg9fSIOEfSUcBlwK/R+Xn8\ndeCwiAhJN2bnuUnSldl5vibprcALI2KjpNcB/zoizpzgzc8lSbHc/8+PPAJr1nT+NDOz4iSV8/99\nigGrl6TtwDER8dCA1+O+Ie0Lh6yiAQsaFbLKCFjgkFWKfpWxeQpZo2ozUeWqu2rhpTo7tYB1HPCB\niDg1+/ocICLi/Nw+HwW+ERGfzb7eSuedrR3UVtKtwAkRsUPSs4B2RLyg9/iSrgI+SGcBousi4qhs\n+5lZ+7dKujo7z42SVgD3RcRBE7z5uTRKwHr0UTjoIPj5z2fUKTOzhisrYCU7B6tHMMEQwdIksOBF\nWTwfa4ZafZ6XOVRw0LGGnqPm98WCnuGCYzwmDFeJfp8eDPwk9/Xd2bZR9hnWdnVE7ACIiPuAZw44\n1j25Y9094Fh720TE48DDM73dxhzwHCwzszTVImBFxPMiYuJfy3vBi/KlFLIqW1Vw3GXbu8/LCFnL\nHaPv6w0IV13jhqwC4Sql2xQUNMkvqcoc4uA7NpWsOwerBgNRzMzmSi0CVhISWfAilVUFoVn3x5rK\n0u3DFAlZ/dq2xjlAQ4LW9p2jP4ZZJlxVc6uD64Hzc48l7gHyi0Z056n27vPsPvsMa3ufpNUA2RDB\n7pzXYcfqt31Rm2yI4FOL/KLMltpnn85jz56qe2JmZnnLTCJpjh3fKmEu1jcpZz5WAjbs/H5pi14U\ntYFNpc3HmrnW7uXnYpVt+86lIas92y40Rp9FM7bctWFx/FgFUGbIGjXgvmjYizcBh0p6DvBT4Ezg\n9T37XAG8DfhsNmfr4Wxu1QND2l4BvIlOqnsj8KXc9sskXUhn6N+hwLezRS4ekXRs1qc3ABfn2rwR\nuBF4DXDdiG/cxrByZWeY4IoVVffEzMy65qqC5aGCizVtPlbtqljTtOxCFw2pYhUxZEXCLXdt2Hsz\n47IWdSlTNqfpbOAa4AfA5RGxVdJZkv4g2+dKYLuk24H/Cmwc1jY79PnASZJ+RGeVwfOyNj8EPgf8\nELgS2JhbgeFtwKV0VibcFhFXZ9svBQ6UtA34Y+CcqVyMObfvvl6q3cwsNbVYRXA5y60imJfEsu3Q\nqFUFIa2VBYusKjj1FQXbuW1FV/sbNsyw77F7Q9Wpxc5fZyMu9z6dVQQ/PUHL3y3l/FYfo6wiCPC0\np8Edd3T+NDOzYuZtFcHSJFHFKonnY/VXpJLViCrWyMFtTqtYfeZcDdKtZJmlyhUsM7P0zF3AKkUi\nC16UJaWQlcKS2BOFrHFueluWpt/vahoGhKvlQpZZqrpzsMzMLB1zGbBKqWIlErJSWrq9LCnMx6ot\nh67BBqwW2H00onppc8cVLDOz9MxlwGqaJg4VrDpkTa2K1Rr/sEMVDlRzMkxwyFLs3cfAkDXrVSLN\nxuAKlplZeuY2YDWpilWW1EKWjagbsly96m+Z+1x1H31DVnull8C3pLmCZWaWnrkNWNCskJXS0u2Q\nznysJKtY01AoXM1JFWuAgd/z3XDVnmFnzMbkCpaZWXo89qVB9K1ylm5PSRk3IV7P5olXglu3ZlND\nFjmY4xC1fSfZ3YKBlWxpLb2RcHew4GbWd/6+8+HKlUFLmCtYZmbpmesKFjSrigWej5WEqqpYhTQ8\ngG3fuRCY2iuX3EjY4crqat99XcEyM0vN3Aes0iQUssqQWsgqar7vjdXw8DSqoSHL4crqaeVKV7DM\nzFLjgEWzbj4Mno81uB8zXLq9t4rVmt2pbYgBIcvhygaR9GeSvivpO5KulvSs3GvnStomaaukk3Pb\nj5H0PUm3Sboot30/SZdnbf5e0pre843LFSwzs/Q4YGX6haz2I2MeJKEq1rghq319OeedpjKHCv5j\n++ax9q9vFesq4Idj7NsEy3wz9wlZDlc2xAUR8eKIeAnwVeADAJKOAl4LHAmcClwiSVmbjwBvjojD\ngcMlvTLb/mZgZ0QcBlwEXFC0c6lUsNrtdtVdGEud+lunvoL7O23ubz04YA0xdsAqSwVDBdsDAllK\nVSwoHrK6VayfjRmwJpLMXKytY+zbhJA1wm8XekKWw5UNEhE/y335ZGBP9vx04PKI2B0RdwDbgGOz\nCtdTIuKmbL9PAmdkz18NfCJ7/j+BlxftXyoVrLp9iKpTf+vUV3B/p839rYfGBKwyPhYms+AFeOn2\nKZp0qGB9q1jW16KQhcOVDSTpQ5LuAn4b+I/Z5oOBn+R2uyfbdjBwd2773dm2RW0i4nHgYUmrKCCV\nCpaZmS1oTMBKSlkhqwSphawy1GpVwWFVrLWFPleNYNJfOzShijWibshyuJprkq7N5kx1H7dkf/4G\nQET8SUSsAS4D/qjMUxc9wNvfDkcfXUZXzMysLIqIqvtQmKT6vwkzS1pEFP4wLOkO4DkTNL0zIp5b\n9PxWjKRnA1+NiBdJOgeIiDg/e+1qOvOz7gS+ERFHZtvPBE6IiLd294mIGyWtAH4aEc8ccC7/v2Zm\nVoEy/r9vxI2Gy7gQZmbT5pBUP5IOjYjbsy/PAG7Nnl8BXCbpQjpD/w4Fvh0RIekRSccCNwFvAC7O\ntXkjcCPwGuC6Qef1/2tmZvXViIBlZmY2JedJOpzO4hZ3An8IEBE/lPQ5Ost07gI2xsKQkLcBHwee\nAFwZEVdn2y8FPiVpG/AgcObM3oWZmc1MI4YImpmZmZmZpcCLXIxA0rsk7Sm62lPqJF2Q3TBzi6T/\nJempVfepbJJOkXRrdgPQ91Tdn2mSdIik6yT9IJu0//aq+zRtkvaRdLOkK6rui1k/o/wMknRxdjPi\nLZLWjdM2gf6+JLf9jtxNmr+dQn8lHSHp/0j6J0nvHKdtgv1N8fr+dtan70q6XtKLRm2bYH9T/qjK\nNQAAB3FJREFUvL6n5/sk6fhR2ybY3+Sub26/X5O0S9Jvjtt2r4jwY8gDOAS4GtgOrKq6P1N+r68A\n9smenwf8VdV9Kvn97QPcTmeRgX2BLcALqu7XFN/vs4B12fP9gR81+f1m7/PfA58Grqi6L3740fsY\n5WcQnZsWfzV7/lLghlHbptTf7Ov/Czw9set7ILAe+HPgneO0Tam/CV/f44ADsuen1OD7t29/E76+\nT8o9fyGwNfHr27e/qV7f3H7/G/gK8JuTXl9XsJZ3IfDuqjsxCxHx9Yjo3kTzBjrhskmOBbZFxJ0R\nsQu4nM6NPxspIu6LiC3Z85/RuePwwcNb1ZekQ4DTgP9WdV/MBhjlZ9Cr6dycmIi4EThA0uoR26bU\nX+gsQz/LzxnL9jciHoiIzUDvPTSSvL5D+gtpXt8bIuKR7MsbWPg/J9XrO6i/kOb1/Xnuy/1ZuPF5\nqtd3UH8hweub+SM6N4K/f4K2ezlgDSHpdOAnEXFL1X2pwL+jeTdE6r0xaP4GoI0m6bnAOjqrlzVV\n95chnlhqqRrlZ9Cgfar4+TVJf+/J7RPAtZJukvSWqfVycF/GuUapXt9hUr++v8/C54g6XN98fyHR\n6yvpDElbgS/T+aw2ctuSFekvJHh9Jf0KcEZEfITF9ykc+/rO/SqCkq4FVuc30flL/xPgvcBJPa/V\n2pD3+76I+HK2z/uAXRHxmQq6aCWTtD+d38a8I6tkNY6kVwE7ImKLpBYN+Ldqlqnz9/LxEfFTSQfR\n+SC1NSKur7pTDZLs9ZX0L4HfA15WdV9GMaC/SV7fiPgi8EVJLwM+xOLPqckZ0t8Ur+9FQCnz1+Y+\nYEVE329MSUcDzwW+K0l0hsttlnRsRNzfr00dDHq/XZLeRGeY1Ykz6dBs3QOsyX19SLatsSStpBOu\nPhURX6q6P1N0PHC6pNOAJwJPkfTJiHhDxf0yyxvlZ9A9wLP77LPfCG3LVqS/RMRPsz//n6Qv0Blm\nM80PUEV+xlfx/0Ohc6Z6fbOFIj4GnBIRD43TtmRF+pvs9c3173pJz1NnAbZkr29Xvr8RsTPR67sB\nuDz73H8gcKqk3SO2XWxWk8vq/qCzyMXMJuNV9B5PAX4APKPqvkzp/a1gYZLifnQmKR5Zdb+m/J4/\nCfyXqvsx4/d8Al7kwo8EH6P8DKLzC67uohHHsbBIwMx/fhXs75OA/bPnTwa+BZxcdX9z+34AeNck\nbRPpb5LXl86H0G3AcZO+10T6m+r1fX7u+TF0prGkfH0H9TfJ69uz/39nYZGLsa/v3FewxhDUe6jG\nKD5M5xvn2k5454aI2Fhtl8oTEY9LOhu4hs78w0sjYmvF3ZqabDnU3wFukfQdOt/D742Fm56a2QwN\n+hkk6azOy/GxiLhS0mmSbgcepTNsqZKfX0X6S2co+hckBZ3RMpdFxDVV9zdbgGMT8BRgj6R3AEdF\nxM9SvL6D+gscRILXF3g/sAq4JKsC7IqIY1P9/h3UXxL9/gV+S9IbgF8CvwBeO6xtqv0l3eu7qMly\nbYedzzcaNjMzMzMzK4lXETQzMzMzMyuJA5aZmZmZmVlJHLDMzMzMzMxK4oBlZmZmZmZWEgcsMzMz\nMzOzkjhgmZmZmZmZlcQBy8zMzMzMrCQOWGZmZmZmZiVxwLK5IOlQSc+suh9mZmZm1mwOWFZbklZL\n+gtJ542w+x8A/zjtPpmZmZnZfHPAstqKiB3At4Ejh+0n6Z8BKyLiF7ltT5P0QUm/kHSNpLNzr/2b\nbPunJR0ztTdgZmZmZo2zsuoOmBW0Dvj6MvucAXwpvyEiHpZ0CfB+4KyI2A4gaRWwGjgiIu6aQn/N\nzMzMrMFcwbK6O5HlA9YJEfF3fbafBNyRC1fHAydHxN86XJmZmZnZJBywrLYkPRF4dkRslfQqSRdK\nelSScvv8CnDvgEO8ArhW0gpJfwE8OSIun0HXzczMzKyhHLCszl4GbJP0u8DNwLuAIyMicvv8DvDp\nAe1fDvwYeAtwWnY8MzMzM7OJOWBZnZ0I/ILOUL9jImJPn6F9z4uIO3obSjoCOBj4cUR8FLgA2JhV\nxfqS9HxJm0vrvZmZmZk1jgOW1dmJwLuBPwc+BSDp6O6Lkl4K3Dig7UnAdyLi89nXn6OzjPvvDznf\ng8APCvbZzMzMzBrMActqSdJTgUMiYhvwDyzMs3p5brfXAP9jwCFeQW5xjIh4HLgIeKekRf8uJL1F\n0qnAh4Bry3kHZmZmZtZEDlhWV78KXAUQEfcD10v6Q+ArsPfeVysj4tF8I0nrJf0lcDJwlKRTsu0H\nAuuBNcDnJB2ebT8NODAirgKe1D2nmZmZmVk/WrwegFkzSHodcH9EfKPgcf4W+FhEfFfSF4F3RMSd\npXTSzMzMzBrHFSxrqhOLhqvMF4B/Lul04A46lTMzMzMzs75cwbLGkXQA8LaI+Muq+2JmZmZm88UB\ny8zMzMzMrCQeImhmZmZmZlYSBywzMzMzM7OSOGCZmZmZmZmVxAHLzMzMzMysJA5YZmZmZmZmJXHA\nMjMzMzMzK4kDlpmZmZmZWUkcsMzMzMzMzEry/wEhP6ZLgi7MbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9864ecff10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,5))\n",
    "\n",
    "ax1 = fig.add_subplot(121)\n",
    "cax = ax1.contourf(k_ACC*Rd_ACC[1], l_ACC*Rd_ACC[1], w.imag[0], 20)\n",
    "cbar = fig.colorbar(cax, orientation='vertical')\n",
    "ax1.set_xlabel(r'$k/K_d$', fontsize=14)\n",
    "ax1.set_ylabel(r'$l/K_d$', fontsize=14)\n",
    "ax1.set_title(r'$\\sigma$', fontsize=18)\n",
    "\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax2.plot(np.reshape(psi[:, 0], (len(zpsi), psi.shape[-1]**2))[:, np.nanargmax(sig.imag)], -zpsi)\n",
    "ax2.set_ylabel(r'Depth [m]', fontsize=12)\n",
    "ax2.set_title(r'$\\hat{\\psi}(z)$', fontsize=18)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2 (oceanmodes)",
   "language": "python",
   "name": "oceanmodes"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
